{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型安全\n",
    "\n",
    "## 概述\n",
    "\n",
    "本次体验流程介绍MindArmour提供的模型安全防护手段，引导您快速使用MindArmour，为您的AI模型提供一定的安全防护能力。\n",
    "\n",
    "AI算法设计之初普遍未考虑相关的安全威胁，使得AI算法的判断结果容易被恶意攻击者影响，导致AI系统判断失准。攻击者在原始样本处加入人类不易察觉的微小扰动，导致深度学习模型误判，称为对抗样本攻击。MindArmour模型安全提供对抗样本生成、对抗样本检测、模型防御、攻防效果评估等功能，为AI模型安全研究和AI应用安全提供重要支撑。\n",
    "\n",
    "- 对抗样本生成模块支持安全工程师快速高效地生成对抗样本，用于攻击AI模型。\n",
    "- 对抗样本检测、防御模块支持用户检测过滤对抗样本、增强AI模型对于对抗样本的鲁棒性。\n",
    "- 评估模块提供多种指标全面评估对抗样本攻防性能。\n",
    "\n",
    "接下来通过图像分类任务上的对抗性攻防，以攻击算法FGSM和防御算法NAD为例，体验MindArmour在对抗攻防上的使用方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 整体流程\n",
    "\n",
    "1. 准备环节。下载MNIST数据集，配置运行信息和数据处理。\n",
    "2. 预训练模型。定义LeNet5网络，训练模型，生成CheckPoint文件。\n",
    "3. 建立被攻击模型。加载预训练模型，测试模型精度，攻击模型，测试攻击后模型精度。\n",
    "4. 对抗性防御。防御实现和防御效果分析。\n",
    "\n",
    "> 本次体验流程支持硬件平台为：\n",
    "> - CPU：在配置运行信息环节配置`context.set_context`中的`device_target`参数为`device_target=\"CPU\"`（本次体验默认使用CPU硬件平台）。\n",
    "> - GPU：在配置运行信息环节配置`context.set_context`中的`device_target`参数为`device_target=\"GPU\"`。\n",
    "> - Ascend：在配置运行信息环节配置`context.set_context`中的`device_target`参数为`device_target=\"Ascend\"`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备环节\n",
    "\n",
    "###  下载数据集\n",
    "\n",
    "本次体验使用MNIST数据集，运行以下一段代码下载MNIST数据集并解压到当前工作目录下的`./datasets/MNIST_Data/`目录里。\n",
    "\n",
    "> MNIST数据集下载地址为：<https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip>。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-12-01 16:52:49--  https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip\n",
      "Resolving proxy-notebook.modelarts-dev-proxy.com (proxy-notebook.modelarts-dev-proxy.com)... 192.168.0.172\n",
      "Connecting to proxy-notebook.modelarts-dev-proxy.com (proxy-notebook.modelarts-dev-proxy.com)|192.168.0.172|:8083... connected.\n",
      "Proxy request sent, awaiting response... 200 OK\n",
      "Length: 10754903 (10M) [application/zip]\n",
      "Saving to: ‘MNIST_Data.zip’\n",
      "\n",
      "MNIST_Data.zip      100%[===================>]  10.26M  --.-KB/s    in 0.05s   \n",
      "\n",
      "2020-12-01 16:52:49 (188 MB/s) - ‘MNIST_Data.zip’ saved [10754903/10754903]\n",
      "\n",
      "Archive:  MNIST_Data.zip\n",
      "   creating: datasets/MNIST_Data/test/\n",
      "  inflating: datasets/MNIST_Data/test/t10k-images-idx3-ubyte  \n",
      "  inflating: datasets/MNIST_Data/test/t10k-labels-idx1-ubyte  \n",
      "   creating: datasets/MNIST_Data/train/\n",
      "  inflating: datasets/MNIST_Data/train/train-images-idx3-ubyte  \n",
      "  inflating: datasets/MNIST_Data/train/train-labels-idx1-ubyte  \n"
     ]
    }
   ],
   "source": [
    "!wget https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip -N\n",
    "!unzip -o MNIST_Data.zip -d datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MNIST数据集下载完成后，查看此时当前工作目录下`datasets`目录结构。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasets\n",
      "└── MNIST_Data\n",
      "    ├── test\n",
      "    │   ├── t10k-images-idx3-ubyte\n",
      "    │   └── t10k-labels-idx1-ubyte\n",
      "    └── train\n",
      "        ├── train-images-idx3-ubyte\n",
      "        └── train-labels-idx1-ubyte\n",
      "\n",
      "3 directories, 4 files\n"
     ]
    }
   ],
   "source": [
    "!tree -L 3 datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中：\n",
    "- `t10k-images-idx3-ubyte`为测试图像数据文件。\n",
    "- `t10k-labels-idx1-ubyte`为测试图像标签文件。\n",
    "- `train-images-idx3-ubyte`为训练图像数据文件。\n",
    "- `train-labels-idx1-ubyte`为训练图像标签文件。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 配置运行信息\n",
    "\n",
    "\n",
    "运行以下两段代码配置运行环境。其中：\n",
    "- `device_target`：指定运行环境，本次体验流程基于CPU环境，配置`device_target=\"CPU\"`。\n",
    "- `set_level`：指定LOGGER输出等级，此处配置为`LOGGER.set_level(1)`。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current version of MindSpore installed is: 1.0.1\n",
      "Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple\n",
      "Collecting mindarmour==1.0.1\n",
      "  Downloading https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.1/MindArmour/x86_64/mindarmour-1.0.1-cp37-cp37m-linux_x86_64.whl (223 kB)\n",
      "\u001b[K     |████████████████████████████████| 223 kB 77.4 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.3.3 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from mindarmour==1.0.1) (1.3.3)\n",
      "Requirement already satisfied: matplotlib>=3.2.1 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from mindarmour==1.0.1) (3.3.2)\n",
      "Requirement already satisfied: scikit-learn>=0.21.2 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from mindarmour==1.0.1) (0.23.2)\n",
      "Requirement already satisfied: numpy>=1.17.0 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from mindarmour==1.0.1) (1.17.5)\n",
      "Requirement already satisfied: Pillow>=2.0.0 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from mindarmour==1.0.1) (8.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from matplotlib>=3.2.1->mindarmour==1.0.1) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from matplotlib>=3.2.1->mindarmour==1.0.1) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from matplotlib>=3.2.1->mindarmour==1.0.1) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from matplotlib>=3.2.1->mindarmour==1.0.1) (2.4.7)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from matplotlib>=3.2.1->mindarmour==1.0.1) (2020.11.8)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from scikit-learn>=0.21.2->mindarmour==1.0.1) (0.17.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from scikit-learn>=0.21.2->mindarmour==1.0.1) (2.1.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/ma-user/anaconda3/envs/MindSpore-1.0.1/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib>=3.2.1->mindarmour==1.0.1) (1.15.0)\n",
      "Installing collected packages: mindarmour\n",
      "Successfully installed mindarmour-1.0.1\n"
     ]
    }
   ],
   "source": [
    "import mindspore\n",
    "\n",
    "msversion  = mindspore.__version__\n",
    "print(\"Current version of MindSpore installed is:\", msversion)\n",
    "masource = \"https://ms-release.obs.cn-north-4.myhuaweicloud.com/{0}/MindArmour/x86_64/mindarmour-{0}-cp37-cp37m-linux_x86_64.whl\".format(msversion)\n",
    "!pip install $masource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current env loaded: \n",
      "    mode: 0\n",
      "    device_target: CPU\n"
     ]
    }
   ],
   "source": [
    "from mindspore import context\n",
    "from mindarmour.utils.logger import LogUtil\n",
    "\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target=\"CPU\")\n",
    "LOGGER = LogUtil.get_instance()\n",
    "LOGGER.set_level(1)\n",
    "\n",
    "print(\"The current env loaded: \\n    mode: {}\\n    device_target: {}\".format(context.get_context(\"mode\"), context.get_context(\"device_target\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据处理\n",
    "\n",
    "利用MindSpore的`dataset`模块提供的`MnistDataset`接口加载MNIST数据集，定义函数`generate_mnist_dataset`对原始数据进行预处理操作，以创建可用于训练和测试的数据集。利用数据加载函数`generate_mnist_dataset`载入数据生成训练数据集`ds_train`和测试数据集`ds_test`，并打印数据集`ds_train`第一组共32张训练图像信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 32 images with label of the first batch in ds_train are showed below:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mindspore.dataset as ds\n",
    "import numpy as np\n",
    "import mindspore.dataset.vision.c_transforms as CV\n",
    "import mindspore.dataset.transforms.c_transforms as C\n",
    "from mindspore.dataset.vision import Inter\n",
    "from mindspore import dtype as mstype\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# generate testing data\n",
    "def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1,\n",
    "                           num_parallel_workers=1, sparse=True):\n",
    "    \"\"\"\n",
    "    create dataset for training or testing\n",
    "    \"\"\"\n",
    "    # define dataset\n",
    "    ds1 = ds.MnistDataset(data_path)\n",
    "\n",
    "    # define operation parameters\n",
    "    resize_height, resize_width = 32, 32\n",
    "    rescale = 1.0 / 255.0\n",
    "    shift = 0.0\n",
    "\n",
    "    # define map operations\n",
    "    resize_op = CV.Resize((resize_height, resize_width),\n",
    "                          interpolation=Inter.LINEAR)\n",
    "    rescale_op = CV.Rescale(rescale, shift)\n",
    "    hwc2chw_op = CV.HWC2CHW()\n",
    "    type_cast_op = C.TypeCast(mstype.int32)\n",
    "\n",
    "    # apply map operations on images\n",
    "    if not sparse:\n",
    "        one_hot_enco = C.OneHot(10)\n",
    "        ds1 = ds1.map(operations=one_hot_enco, input_columns=\"label\",\n",
    "                      num_parallel_workers=num_parallel_workers)\n",
    "        type_cast_op = C.TypeCast(mstype.float32)\n",
    "    ds1 = ds1.map(operations=type_cast_op, input_columns=\"label\",\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "    ds1 = ds1.map(operations=resize_op, input_columns=\"image\",\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "    ds1 = ds1.map(operations=rescale_op,input_columns=\"image\",\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "    ds1 = ds1.map(operations=hwc2chw_op, input_columns=\"image\",\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "\n",
    "    # apply DatasetOps\n",
    "    buffer_size = 10000\n",
    "    ds1 = ds1.shuffle(buffer_size=buffer_size)\n",
    "    ds1 = ds1.batch(batch_size, drop_remainder=True)\n",
    "    ds1 = ds1.repeat(repeat_size)\n",
    "\n",
    "    return ds1\n",
    "\n",
    "batch_size = 32\n",
    "ds_train = generate_mnist_dataset(\"./datasets/MNIST_Data/train/\")\n",
    "ds_test = generate_mnist_dataset(\"./datasets/MNIST_Data/test/\", batch_size=batch_size, sparse=False)\n",
    "\n",
    "print(\"The 32 images with label of the first batch in ds_train are showed below:\")\n",
    "ds_iterator = ds_train.create_dict_iterator()\n",
    "next(ds_iterator)\n",
    "batch_1 = next(ds_iterator)\n",
    "batch_image = batch_1[\"image\"].asnumpy()\n",
    "batch_label = batch_1[\"label\"].asnumpy()\n",
    "%matplotlib inline\n",
    "plt.figure(dpi=144)\n",
    "for i,image in enumerate(batch_image):\n",
    "    plt.subplot(4, 8, i+1)\n",
    "    plt.subplots_adjust(wspace=0.2, hspace=0.2)\n",
    "    image = np.squeeze(image, 0)\n",
    "    image = image/np.amax(image)\n",
    "    image = np.clip(image, 0, 1)\n",
    "    plt.imshow(image, cmap=\"gray\")\n",
    "    plt.title(f\"image {i+1}\\nlabel:  {batch_label[i]}\", y=-0.65, fontdict={\"fontsize\":8})\n",
    "    plt.axis('off')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预训练模型\n",
    "\n",
    "本次流程以LeNet5模型为例。运行以下一段代码，定义LeNet5网络模型，训练模型并保存CheckPoint文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============== Starting Training ==============\n",
      "epoch: 1 step: 375, loss is 2.2983687\n",
      "epoch: 1 step: 750, loss is 2.3185787\n",
      "epoch: 1 step: 1125, loss is 2.2873642\n",
      "epoch: 1 step: 1500, loss is 2.3000097\n",
      "epoch: 1 step: 1875, loss is 2.2676728\n",
      "Epoch time: 19415.527, per step time: 10.355\n",
      "epoch: 2 step: 375, loss is 1.8463521\n",
      "epoch: 2 step: 750, loss is 0.30592605\n",
      "epoch: 2 step: 1125, loss is 0.13993229\n",
      "epoch: 2 step: 1500, loss is 0.33774194\n",
      "epoch: 2 step: 1875, loss is 0.06339652\n",
      "Epoch time: 18092.739, per step time: 9.649\n",
      "epoch: 3 step: 375, loss is 0.22111271\n",
      "epoch: 3 step: 750, loss is 0.028834635\n",
      "epoch: 3 step: 1125, loss is 0.018264536\n",
      "epoch: 3 step: 1500, loss is 0.137757\n",
      "epoch: 3 step: 1875, loss is 0.03770245\n",
      "Epoch time: 17978.534, per step time: 9.589\n",
      "epoch: 4 step: 375, loss is 0.20166841\n",
      "epoch: 4 step: 750, loss is 0.08258131\n",
      "epoch: 4 step: 1125, loss is 0.006526878\n",
      "epoch: 4 step: 1500, loss is 0.048344947\n",
      "epoch: 4 step: 1875, loss is 0.031841453\n",
      "Epoch time: 17969.653, per step time: 9.584\n",
      "epoch: 5 step: 375, loss is 0.14083362\n",
      "epoch: 5 step: 750, loss is 0.075226724\n",
      "epoch: 5 step: 1125, loss is 0.0012143524\n",
      "epoch: 5 step: 1500, loss is 0.023025677\n",
      "epoch: 5 step: 1875, loss is 0.0073616193\n",
      "Epoch time: 17980.884, per step time: 9.590\n",
      "epoch: 6 step: 375, loss is 0.0020197192\n",
      "epoch: 6 step: 750, loss is 0.0038225418\n",
      "epoch: 6 step: 1125, loss is 0.082278654\n",
      "epoch: 6 step: 1500, loss is 0.006920913\n",
      "epoch: 6 step: 1875, loss is 0.0039766827\n",
      "Epoch time: 17971.716, per step time: 9.585\n",
      "epoch: 7 step: 375, loss is 0.024966951\n",
      "epoch: 7 step: 750, loss is 0.03923794\n",
      "epoch: 7 step: 1125, loss is 0.0041903164\n",
      "epoch: 7 step: 1500, loss is 0.0011266943\n",
      "epoch: 7 step: 1875, loss is 0.009464508\n",
      "Epoch time: 17993.664, per step time: 9.597\n",
      "epoch: 8 step: 375, loss is 0.058243666\n",
      "epoch: 8 step: 750, loss is 0.011001761\n",
      "epoch: 8 step: 1125, loss is 0.056092307\n",
      "epoch: 8 step: 1500, loss is 0.0283611\n",
      "epoch: 8 step: 1875, loss is 0.0043233447\n",
      "Epoch time: 17954.005, per step time: 9.575\n",
      "epoch: 9 step: 375, loss is 0.0030290876\n",
      "epoch: 9 step: 750, loss is 0.043453947\n",
      "epoch: 9 step: 1125, loss is 0.0010939447\n",
      "epoch: 9 step: 1500, loss is 0.089483395\n",
      "epoch: 9 step: 1875, loss is 0.0005572842\n",
      "Epoch time: 17826.415, per step time: 9.507\n",
      "epoch: 10 step: 375, loss is 0.021585621\n",
      "epoch: 10 step: 750, loss is 0.0018079067\n",
      "epoch: 10 step: 1125, loss is 0.0066049187\n",
      "epoch: 10 step: 1500, loss is 0.095723055\n",
      "epoch: 10 step: 1875, loss is 0.0016937024\n",
      "Epoch time: 17891.755, per step time: 9.542\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import mindspore.nn as nn\n",
    "from mindspore.common.initializer import TruncatedNormal\n",
    "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor\n",
    "from mindspore import Model\n",
    "from mindspore.nn.metrics import Accuracy\n",
    "\n",
    "def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n",
    "    weight = weight_variable()\n",
    "    return nn.Conv2d(in_channels, out_channels,\n",
    "                     kernel_size=kernel_size, stride=stride, padding=padding,\n",
    "                     weight_init=weight, has_bias=False, pad_mode=\"valid\")\n",
    "\n",
    "\n",
    "def fc_with_initialize(input_channels, out_channels):\n",
    "    weight = weight_variable()\n",
    "    bias = weight_variable()\n",
    "    return nn.Dense(input_channels, out_channels, weight, bias)\n",
    "\n",
    "\n",
    "def weight_variable():\n",
    "    return TruncatedNormal(0.02)\n",
    "\n",
    "\n",
    "class LeNet5(nn.Cell):\n",
    "    \"\"\"\n",
    "    Lenet network\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super(LeNet5, self).__init__()\n",
    "        self.conv1 = conv(1, 6, 5)\n",
    "        self.conv2 = conv(6, 16, 5)\n",
    "        self.fc1 = fc_with_initialize(16*5*5, 120)\n",
    "        self.fc2 = fc_with_initialize(120, 84)\n",
    "        self.fc3 = fc_with_initialize(84, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.flatten = nn.Flatten()\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.max_pool2d(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.max_pool2d(x)\n",
    "        x = self.flatten(x)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "lr = 0.01\n",
    "momentum = 0.9\n",
    "network = LeNet5()\n",
    "model_path = \"./ckpt\"\n",
    "# clean old run files in linux\n",
    "os.system('rm -f {0}*.ckpt {0}*.meta {0}*.pb'.format(model_path))\n",
    "\n",
    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
    "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n",
    "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
    "config_ck = CheckpointConfig(save_checkpoint_steps=1875,\n",
    "                             keep_checkpoint_max=10)\n",
    "ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", directory=model_path, config=config_ck)\n",
    "model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n",
    "\n",
    "print(\"============== Starting Training ==============\")\n",
    "model.train(epoch=10, train_dataset=ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor(per_print_times=375)],\n",
    "                dataset_sink_mode=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立被攻击模型\n",
    "\n",
    "以MNIST为示范数据集，自定义的简单模型LeNet5网络模型作为被攻击模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载LeNet模型文件\n",
    "\n",
    "运行以下一段代码，加载预训练的LeNet模型文件（`checkpoint_lenet-10_1875.ckpt`），并打印输出网络中载入的所有的参数名。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The names of all params are:  ['conv1.weight', 'conv2.weight', 'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight', 'fc3.bias']\n"
     ]
    }
   ],
   "source": [
    "from mindspore.train.serialization import load_checkpoint, load_param_into_net\n",
    "\n",
    "\n",
    "ckpt_name = './ckpt/checkpoint_lenet-10_1875.ckpt'\n",
    "net = LeNet5()\n",
    "load_dict = load_checkpoint(ckpt_name)\n",
    "load_param_into_net(net, load_dict)\n",
    "\n",
    "# get all names of parameters in net.\n",
    "h = network.parameters_dict(recurse=True)\n",
    "print(\"The names of all params are: \", [i for i in h.keys()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试攻击前模型精度\n",
    "\n",
    "从训练数据集`ds_test`中抽取前3组共96张数据图像和标签用于测试预训练模型精度。运行以下一段代码，测试预训练模型对被攻击之前的测试图像进行预测的精度。通过输出INFO信息可以看到，测试结果中分类精度达到了97.9%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:11,287 [<ipython-input-8-2ca8b2dce53a>:26] [demo] prediction accuracy before attacking is : 0.9791666666666666\n"
     ]
    }
   ],
   "source": [
    "from mindspore import Tensor\n",
    "\n",
    "\n",
    "# prediction accuracy before attack\n",
    "TAG = 'demo'\n",
    "model = Model(net)\n",
    "batch_num = 3  # the number of batches of attacking samples\n",
    "test_images = []\n",
    "test_labels = []\n",
    "predict_labels = []\n",
    "i = 0\n",
    "for data in ds_test.create_tuple_iterator():\n",
    "    i += 1\n",
    "    images = data[0].asnumpy().astype(np.float32)\n",
    "    labels = data[1].asnumpy()\n",
    "    test_images.append(images)\n",
    "    test_labels.append(labels)\n",
    "    pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),\n",
    "                            axis=1)\n",
    "    predict_labels.append(pred_labels)\n",
    "    if i >= batch_num:\n",
    "        break\n",
    "predict_labels = np.concatenate(predict_labels)\n",
    "true_labels = np.argmax(np.concatenate(test_labels), axis=1)\n",
    "accuracy = np.mean(np.equal(predict_labels, true_labels))\n",
    "LOGGER.info(TAG, \"prediction accuracy before attacking is : %s\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行以下一段代码，打印测试数据图像信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test images before attacking showed below:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1200 with 96 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(100.0, 120.0), dpi=10)\n",
    "for j,images in enumerate(test_images):\n",
    "    for i,image in enumerate(images):\n",
    "        plt.subplot(12, 8, i+1+32*j)\n",
    "        plt.subplots_adjust(wspace=0.3)\n",
    "        image = np.squeeze(image, 0)\n",
    "        image = image/np.amax(image)\n",
    "        image = np.clip(image, 0, 1)\n",
    "        plt.imshow(image, cmap='gray', interpolation='nearest')\n",
    "        # plt.title(f\"image {i+1}\\nlabel:  {batch_label[i]}\", y=-0.65, fontdict={\"fontsize\":8})\n",
    "        plt.axis('off')   \n",
    "print(\"Test images before attacking showed below:\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 攻击模型\n",
    "\n",
    "调用MindArmour提供的FGSM接口（`FastGradientSignMethod`），使用被攻击前抽取的96张数据图像`test_images`作为被攻击数据集，保存被攻击后数据集图像到当前notebook目录下的`ada_data`文件中。其中，参数`eps`为攻击对数据范围产生的单步对抗性摄动的比例，该值越大，则攻击程度越大。关于`FastGradientSignMethod`的详细使用说明，可参考[官方API文档](https://www.mindspore.cn/doc/api_python/zh-CN/master/mindarmour/mindarmour.adv_robustness.attacks.html#mindarmour.adv_robustness.attacks.FastGradientSignMethod)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of adv_datas is : (96, 1, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from mindarmour.adv_robustness.attacks import FastGradientSignMethod\n",
    "\n",
    "\n",
    "# attacking\n",
    "attack = FastGradientSignMethod(net, eps=0.3)\n",
    "start_time = time.perf_counter()\n",
    "adv_data = attack.batch_generate(np.concatenate(test_images),\n",
    "                                 np.concatenate(test_labels), batch_size=32)\n",
    "stop_time = time.perf_counter()\n",
    "np.save('./adv_data', adv_data)\n",
    "print(\"The shape of adv_datas is :\", adv_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试攻击后模型精度\n",
    "\n",
    "对模型进行FGSM无目标攻击后，通过输出信息可以看到：\n",
    "\n",
    "模型精度由97.9%降到5.2%，误分类率高达94.79%，成功攻击的对抗样本的预测类别的平均置信度（ACAC）为 0.716，成功攻击的对抗样本的真实类别的平均置信度（ACTC）为 0.056，同时给出了生成的对抗样本与原始样本的零范数距离、二范数距离和无穷范数距离，平均每个对抗样本与原始样本间的结构相似性为0.3327，平均每生成一张对抗样本所需时间为0.00139s。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:15,382 [<ipython-input-11-bb318910a558>:10] [demo] prediction accuracy after attacking is : 0.052083333333333336\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:15,383 [<ipython-input-11-bb318910a558>:17] [demo] mis-classification rate of adversaries is : 0.9479166666666666\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:15,384 [<ipython-input-11-bb318910a558>:19] [demo] The average confidence of adversarial class is : 0.71637434\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:15,384 [<ipython-input-11-bb318910a558>:21] [demo] The average confidence of true class is : 0.056136377\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:15,391 [<ipython-input-11-bb318910a558>:24] [demo] The average distance (l0, l2, linf) between original samples and adversarial samples are: (1.5742399376497331, 0.44637327849144565, 0.3000000361557812)\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:15,586 [<ipython-input-11-bb318910a558>:27] [demo] The average structural similarity between original samples and adversarial samples are: 0.3327861081112872\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:15,587 [<ipython-input-11-bb318910a558>:29] [demo] The average costing time is 0.0013912176073063165\n"
     ]
    }
   ],
   "source": [
    "from scipy.special import softmax\n",
    "from mindarmour.adv_robustness.evaluations import AttackEvaluate\n",
    "\n",
    "\n",
    "pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()\n",
    "# rescale predict confidences into (0, 1).\n",
    "pred_logits_adv = softmax(pred_logits_adv, axis=1)\n",
    "pred_labels_adv = np.argmax(pred_logits_adv, axis=1)\n",
    "accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))\n",
    "LOGGER.info(TAG, \"prediction accuracy after attacking is : %s\", accuracy_adv)\n",
    "attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),\n",
    "                                 np.concatenate(test_labels),\n",
    "                                 adv_data.transpose(0, 2, 3, 1),\n",
    "                                 pred_logits_adv)\n",
    "\n",
    "LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',\n",
    "            attack_evaluate.mis_classification_rate())\n",
    "LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',\n",
    "            attack_evaluate.avg_conf_adv_class())\n",
    "LOGGER.info(TAG, 'The average confidence of true class is : %s',\n",
    "            attack_evaluate.avg_conf_true_class())\n",
    "LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '\n",
    "            'samples and adversarial samples are: %s',\n",
    "            attack_evaluate.avg_lp_distance())\n",
    "LOGGER.info(TAG, 'The average structural similarity between original '\n",
    "            'samples and adversarial samples are: %s',\n",
    "            attack_evaluate.avg_ssim())\n",
    "LOGGER.info(TAG, 'The average costing time is %s',\n",
    "            (stop_time - start_time)/(batch_num*batch_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印被攻击后的测试图像信息，由输出图像结果可以看出，被攻击后的图像和攻击前相比，成功误导了模型，使模型将其误分类为其他非正确类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test images after attacking showed below:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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rzz77bMvrNlI4J8Dll18OZH3aw9+c6mMfrh9Si2/e+4NmmNXvuVDYYqBMeOH+iyP2ZmZmZmY10FUf+1gk5swzz2z5X08jJ554YsvrP/vZzwDYYYcdOn6Onp60fpNGd+ftV6dxDaLZflX2oRVXXLHlf0XczzrrLCB7Mox9riIwyqXexKhXjCLof/7nfw5k8y+EHnvsMWDkeZGy3377AXDfffcBsMsuu7RdTn3tY33spY7nTa/peI+dX7Hl61TWqVbA1HfdeOONATjyyCOBbAbl2PZjUv3Hdb6E21XUS7nVNfNtE65l4XfccMMNgWxeEs0lk3d9CetCrZNPPvlkqf20tLDMzznnnDHak8ERu2ZonOgnn3zS9n1lfFRk/9NPP215X/f61O8nefzxxwH4z//5PwOwYMGClve1/jvvvANkfftTHLE3MzMzM6uBoeHhYYaGhoar2FjeftzhCPswr70oZ2f4dJQ3WpDX8PDwUKkVe0B1UTTHcxgRzBt1Uv9TRc3efPNNAPbff/+O66mFIOzbn1esrvqxLkKpsg6/29lnnw3EszppNsfY54QUaXzxxRc7rqd+4orYFz0/BqEuqqayU57gWLYQUctXOKYlpk7XqFDqvFh++eWBLHqr82HChAltP2e0Wzn22msvILvfPPzwwy3vaz8GoS5SYtcW3QdUBqLWDI3rCcXq6NJLLwXgoYce6vj5dT4vupX6vROOA9pnn31yrR9up1uDUBexa1RszoxUJi71JtF4Ut3rU7999dv2lVdeAbLzLka/s8KeGeF29bmO2JuZmZmZ1UAleey7ffoO833L3LlzgZH9mOrUTzUmFZEPX4/9n6Jo2R//+Ecg/yjup556KtdydayrbjNEhOuFWTpiFD2+8MILgSzblCgyr3kfwn6CdeznXZWy/aqV0UV9ktVHWZpU1rHvqpli7777bmBkxgmJ1UHsuNX4htgYkjXXXBOASZMmpXYdgPvvvx+Ae+65BxgZZa6D1HF+7bXXAiPrZp111sm1XdWRzoPnn3++7fJNOi/GShPGioyWWFkefvjhAHz/+98H0r/b9Ffj4mLj44pyVhwzMzMzsxrqKmKfirBY9brtL6f1FUVTjujYk2X4ebvtthswMqJTdD+a5KWXXur4ft7ZG1999VVgZJYPef3114GsbmMcue9eeF4cccQRQPEMR3USHlc6DtV3/cADDyy13dtvvx2Al19+ueX1vOMa1O9Vs2RvuummLf+n1Ol+lvf+EF7/NS5CGe9UprHtKvKvbB5WvdiYLfUPVwvU4sWLR22f+l2q77ve/+1vfwtkc2vEfg/pGpKaayMmdS/WNS/Wtz62HUfszczMzMxqoJI89jHdRjo0Al9ZQIp+fp1U/V133XVXIMt1nuq7L+GcAkWz8NRR6qn75ptvBuB//I//AWRP+VpPf5VBQnWi9WT69Oltt6++9Opb3+S6qIoiJCpLZZ6IZTA66qijgHr2y06JjfMpe40JKUNXKttH7FqkaLNaEMaNGwfA2muvDeSP3NdJ7JrV7Yy0Ert3N+me3WvKVx9mxVELsM6/O++8E3DZ56HzIsw4t/322wPxMoxd44p+bkhz3RTNeuiIvZmZmZlZDVSSFScl9pShbDh77rlny+ta7oEHHhiFvau3vFGxbqO8TY7cV/3ddV4oYj9r1iwgixqENBue+iLH9qvJdZQSls0LL7wAZBF69VtVZCZcr8lSx1VqZtjU8XjQQQd1sXcZtWg98cQTABx77LFt9y9slZQ61nXR2YP1f2p+h9TnpfKIW8bX7erlLcuLL74YyFrLU+t1ezxr7hnRTLjh9lMcsTczMzMzq4FRidiH9DRz3nnnAVmfR6te7Gl/2rRpQJbNo+rPa5K8T9EzZswAsr6R48eP77i8zo9TTz0ViJet+oOnnu4dHUtLlY3LbqSyUazYeoqsK+tHeFyH8rYIDA//aTJKZQnR7I/h3B1NqONYXVSVdS1va3sTyrpXYllxRPf4zz//fDR2ZyDlPf4+/vjjtq9XNYOyrkWxOTliYp/niL2ZmZmZWQ0MDQ8PMzQ0NNyLjaf604WjuWXdddcFsly4ixYtanm/6qf84eHh9qkuxkBVdZGKvKhftmZOS1E+YmU5OP/888vvXAd1qIu8T/FTpkwBRmYtKNqXUi0Ayv4Rfm7ZSH0d6qJqV1xxBQDrrbdey+ubbLIJkM5jX/ba1c91ETtedY1ZffXVO27v3HPPBeDpp58GRmYkqro/dt7zK/Y5/VwXZeWN1KvVUfOZhHzv7p3YfSX2O2rfffcF0jnQqzLIdZE6/jWmRLNZV+Wtt94CsvkhUvJeAx2xNzMzMzOrgTHpYx+jp5Fll/3Tbo3Wk2adpfqfqh93Shh5sbRUpPG5555r+3rKp59+CsDll18OjIzUx/bD4vJGQhR9XnHFFVteV6tLk4XHu2ZvnDx5csf1FA1bddVV275fNFLvLFDVURkq138YqQ/L1vfu3okd/9/97ncBOOuss4CRY0akaNaqJkrNhdErea9ZeevKEXszMzMzsxroq4i9nxx7Jxb1qjoS78wr+ansNeOs6H/l0w4pOqyR9C7z6qSiu5pjwDKp3OfdXmO6nQ3V50V+3bZyaMbZqrOtWTyqqwi9xvno/5NPPhmAhQsXjup+DqLUNSPvGJSqWgu7Xd8RezMzMzOzGhiVrDghZZS47LLL2r6vjCuxkcJVRygHeTR3TK+iuL3up1eHuqi67LvNLV1WHeoir6r7ZTcp+0fRXM5jdTxXpZ/roqy8WUE0p0aM7t2jNe6njnWRV1VjR1wXcVXfB1K/n6rKi++IvZmZmZlZDYxpHvu868f4STNutDNBuC4yVfWrG+vIZR3qoi76sS6qii4Nmn6si271ui57tf061sWgcl10zxF7MzMzMzP7//U0Yj8o/KTZP1wX/cN10T9cF/3DdZHm1vbmcV30D0fszczMzMxqYGh4uNEPNmZmZmZmteCIvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDfiHvZmZmZlZDSwLMDQ0NDzWOzKWhoeHh8Z6H8R14broF66L/uG66B+ui/7huugfrov+sexY74CNrW984xu5lvvVr37V4z2pv7xlHUqVfdHtui6tH4THrY9LM7PuuSuOmZmZmVkNDA0PDze+2aKOTUipKG630bFeRfrrWBcxsTIMy2ysWlWaVBf9ro51UbYFK69etQDUsS4Gleuif7gu+ocj9mZmZmZmNdBVxL5XfYZH2yA8aVYVgdd2YlHhstHiovsRMwh1UVRVEfdYHRX9vLx1VMe6CA1KP+9BrIu8LVJ51yu6nV4ZxLpIqfo8KFv3RdWxLlLy3gdi63Ur9rl1qIuy987ROt7zcsTezMzMzKwGetLHvld9J91nsvuordabOHFimd1jzpw5Lf+/++67ANx0000dPy+vQaqLmLCOikZYHnjggUKfN23aNCCri/fff7/Q+jF1qIuUopGWsWqlHMS66HXEPqbXY1QGsS5Seh3N9Zis6oxWdLjofauOdVE0gl923Fz4WyG2Xl6O2JuZmZmZ1UClEftB7XM/CE+aZSP12223HQDf//73W94/8sgju9pu6OqrrwZg/vz5AHzyySct7zfpqb/oU/4yyywDwK9//WsAdtttt1yfE27n2WefBeDMM8/MtX5KE+oipt+yRg1CXZTt+xs64YQTANhyyy1bXtc1RdeYuXPndtxOrzKDDUJd9ErRiGQsEhlbr6gm1EWvW1OKfn6d+9jHpK5t/fbb1xF7MzMzM7MaGNU89r3OWxyTeiqqw5NmWLYbbbQRAJdccknL60X7n6YiLqFDDz0UgPfee6/j9mLqVBepPvY77rgjAKecckrLcimxMnTEvriifR3dx7545q7UNWe99dYDYOWVVwbg4IMPBuCVV15pWW7JkiUAHHTQQUB2bbvqqqsAWLRoEQBnnHFGy3offvghAC+//HL7LxRRp8hk3gi6lL1P5N2PoupUF93KG0E/7rjjALjyyit7vk9Qz7oIy3rChAkArLbaai2vn3POOcDIa0+MxsO9+eabbd/v9n7hiL2ZmZmZWQ0s283KRftUrrLKKh3fV0Tms88+62a3GkV1MDT0p4fl8ePHA91H6lPLdZvvvgliZbLrrrsCcNJJJ7V9f6zHnNRJ0eMyXF7nlaLI+j9F1zBd0/otz/FoSH23cePGAXDFFVe0LB9G6sPl582bB2Rl+p3vfKfj5y5cuBDIWgSUNWp4uP6TU3Y77uGb3/wmACussEJX+3HIIYe0/K+yj9VFnc+LbsVaXfR/eF7tsccebbcTrq/W9liGuzoo20NhpZVWArKI/FprrdWyvK5JeT9frYsnnnhirv0sej44Ym9mZmZmVgNdRexTTxGTJk0C4Nvf/nbb98On+NNOOw3IIixF9yP2NFZVtoZ+EPuOitTnfdqOldmxxx4LwI9+9KNc+6Ht6HNjn6/l61QXMb3Oo51XE6PEMUVn9dX/Rc8rLffb3/4WgBdffLH4zg6oVCQxdMsttwBZdpvllluuJ/u16aabAlkkU32PU+dlHa5V3WZCOeKIIwDYe++92y4Xu+6H9/7wXi96ffHixW0/PzTIdRFTdnb3WFnovCq7/Toc9zFls9po/h7dD/q9bByxNzMzMzOrga4i9qHwqeeDDz4A4n3rw6ee8847r+X/1Ehjva9c7aKsILHIUb8/bbUTe6JU9ps///M/B+Cuu+4CstzosfUefPBBAPbZZ5+W17/73e923A+Vnda/7LLLkvveFKmnfvWtV9nFxI5P9cf7z//5P7e8vuGGGwJZ9g+Pd8ikzvWNN94YyK4h4bUkvPaktqdr3aBEdrpR9DhTxDyMKE6dOhXo3bgdXQvXXnttILvP/Mu//AtQPFtOP+o2u9ltt90GwKWXXtry+u9///uW9UM63jWmS9cgZQ/R++H6GoOy2WabAfnrYhDv4b2aT0HOPvtsIJ2RpWhmrya0mqS+o8ZW6Xoerle2LBYsWABkv59mzJhRajsxjtibmZmZmdVAqYh9KieuXleEMq/w6WerrbZq+3r4fkgRmphBfOqP+eEPfwjkH5V99913A3DNNde0vD59+nQAzj//fCBdNspTXzQ/cpOtu+66QP4xIYoSzJ49G8hmpF111VVbllf+eit+bq+xxhpA/FrSq2tEHa5BefddeeqVpSMl7zVELV+vv/56y+vTpk0DYJtttgHg9NNPB7L7whZbbNGyvlppnnzyyY770891VXTftLz6tt9xxx1A/DxIUctxXrG6qON9JDWfSUysDHS8/vGPfwTy507vtkz7+fjvVuy7qUU3VYevvfYaMDLyroi8roFy2GGHAXD44YeX2q8UR+zNzMzMzGqgkj724dOM/irSOHHixLbLS7c5O/PuVxOFZRnLEZ23jB5++GEAbrjhhq72o4nUShJrUVLU6qOPPgKy/MPKGiJvv/024DJtJ2/2m6eeegqItyqmrknXXnstABtssAEAu+yyS+F9rZtYNEv54/OuHxOOTVEmFl2LtL4yuOi+c+GFFwLZDM+iFjC1YtbxfhHWifq+q4x+85vfAOk5Zsp+bih2fqou8rbqDKKimblkv/32A2DnnXcGRv6eKvu5mvV0nXXWAbLxEUX3bxClznFlddL5ksr0pTEqGjui95WBKxSWYdVl6oi9mZmZmVkNVJoVJ69NNtmk5X/NwhV7/6qrrgLSUTFFcFIz1w7iE2e3M8eG2WwUadTr6ksWo0j95Zdf3nH7qf2us1QdHXXUUUAWgQmXU9/gK6+8EoBf/vKXbbdjxYVl+PWvfx2ArbfeuuNyor7Hak1R9Hf77bdvu7zGPdQpypVX+J2VWSKv8H7w1ltvAVl/1dTYrnB2R2Ve+clPfgJkudmXX355ACZPngzAgQce2PI5gywsmxNOOAGA//2//zcwsvUitb6kjmO1fpS9Zmm9LbfcEsgyvqT2q590O1v7rbfeCsBqq60GZK0ZeWk23zCTS+jdd99tu19SdmxAP8s711Fq/gVRFsJwHoexLitH7M3MzMzMaqCSiH34FKRR2+rbqIiIRt6HT4qp/qxTpkwBsoh97Knr17/+dcf3x/opqhfUh0vfbc899wSy3NASPpFq/EMqUi/q5/3pp5+2vN7rvmKDqOxMguH4h5///OcALF26FIAnnngCyPrYN+k4LypWNop+KW92ank59dRTgSwb1J133tlxeUV/lWklPP+aUEcaS6LjOja+R8f1ueeeW+pzUhFF9SXWXB/qS6yIvah/tzK1aAb0OtSVIuBf/epX276f95oVm4FW1FKlSKbu3bHPCcv2yCOPBLIWhtAg1EXefdTYE12T1GKrsVUrrbRSx+2pLHVvnj9/PgBf/OIXAfh//+//ASNbiPOq0zxAeaUi9KLWQI216rcyccTezMzMzKwGuorYx57yX331VSD+pJh6ulEU+p133gHST1GHHnookEXT6iwsc+VPVVaDMFIvqTLvNrduHfvjVS3MipMqI0V9RXnwH3roISBr+VJEUno90+EgU57hWEtTrOzUhzJVtspLnOq/WqfzJFYmw8PDHdfTLKNlI/V5+waL6lCtmiFFizX7qs4zGeT+3Rrfo0h6KHX8q7VFs1ynjl+9/2//9m9Adi8P83k3QVimitTvtddeQHbvTrUChts755xzgKxP/dVXX92yXGw2U/1OOumkkwA44IADcn1uHaTGOYTXiNjxrWuEWlX2339/AG6//faW5TTPkOZ5GK1riCP2ZmZmZmY1MCZZcVJPLcpKcN9997V9P+y/l4oMNUGqDGJ9G/NGDFdYYYVcy9chAtkrH3zwAZDljg77+KaiZuprrzEsyvKhvsPKBrVkyZKW7VlGUa5whk1lu1H0K8zkUnQ8Q9ksVoOobP73mTNnlvq8vK0dRfdLyykr24knnlhq/8ZS+J11jVFksajnnnsOyMaYFO13rQjovvvu23b9cL9lzpw5QHxMwCDT3BnqiVD0On300UcDIyP02t6NN97Ydj19zre+9S0gq5PYck2Q9xoRvq/7h84LjWH5r//1vxb6PI1ZUQtvbPmiHLE3MzMzM6uBUhH7VDabww47rOX18Okk9qSY1wsvvJBrO3Xqxyp5v8vNN98MjIzkFy0LlaEySagvWSyymVKnuihKWQ9Udsccc0zL+0WjwsrH/c1vfrPl9X322afj9vJufxCVjYxfd911wMg+kqO9H4Oo2+/abc5zibWSlD2+B3Em2nBfdY34xS9+AeRv5fjud78LwPPPP9+y3VSZVFWXDzzwQMftDcI1q+qxTtreJ598AsCOO+7Y8n7ejC7d7kedqQxTZbnWWmsBI1tH8pallktlmSp7DXPE3szMzMysBirpYx8+TYTZP/K67bbbOq6nWVPzzgzYpCdRPdktXry45X/1u46Vad4IiyL1UrbPZh1bUUJ5v1s4hkRP/2Hfy1iZqf/sIEYW+42yd6SuWalrlDQ5G05eZ5xxBpDlPs8rFpmP7c93vvMdIBuLMm7cuLbrh+pQV8ppnvoO+q66x2677bbAyCw2VUfqQ9pP5bHfdNNNK9luP1HLbSxDUSgsW2W+y5vZK1xOGZIG+bjuV3lbtkS/z6rmiL2ZmZmZWQ1UmhVHTyfnn38+AGuuuWbL+2+99VbL/4r6agT8iiuu2Ha7d999NwDrrLNOdTtbc2rViOVM7zbCou0q964yvigaof+bqGzf9vvvvx+Ajz/+uOX166+/Hsjmh5g0aVLH7Wp8hesiE2aB0vGra0/enOXhNUrLqaxvuOGGXPvRBENDQz3ZbiqSrpnONafArFmzgGxG2RRFNMNjoo7C41yzoGpmTR3XseVDsTpR66T6/KdaI3Veaob0Ovn888+BLLd/SGWuMgg5M133Ysex7pXKODR9+nQg3QKb9/WQfiuHuq1DR+zNzMzMzGqgJ3nsn3zyyY7vT5gwAcj6WCqvd5jdI8wrvMEGG7Td3iCPnC8qbx+uW2+9FRiZr1vC9dW3MqQITrhebL+WW2652K43RtnjTpF2/Y2ZOHEiADvvvDOQzfCsz9XMguoPXoe+wkWlvqv6NiqCmLqGLFy4sOP7mpnTyut29mudB8rKpu2F17BUNE1jxJTHfpDPm7KtJmq1UOauTz/9tOPyJ598MhBvHdS9O9YvPKRIqVoOZBDGEeX9PTJ37ty2yylTit7/13/9VwC23nprIGs1VI+IvNeehx9+GIi3KjbxPhFSxiGVgY7r1VZbDcgyMe6yyy4ty4nK8LHHHgNghx12aPs5uib1qlXQEXszMzMzsxqoJGIfG4WdegLU+2GkPrRgwYJud3HgxWb8K0uRkEceeQTI+uSHdaU+9L/97W+BLGoQUv7vZ555BhiZ/7jJUlkKigqjWIpUxjQ5AtPt8acMRZtvvnkVu9OoqJhaRTT7aJgbWnNgaCbOF198Ecj6FquvsWZl1PwPyqKjXOeiKFi3M9JuvPHGwMgo9SBcy8Lvpuvxyy+/DGTjD/JSBDPWoiuaW+akk04qtP2QxtOF1zgZpPOmaF/41LVBEXdRz4ff//73QHxeE1HPBx0LTZa3z7yuOfp7+eWXA/AP//APbdffaKONgGwm89TnpTiPvZmZmZlZg1Waxz5vREN9hFPU1zEcmZ/ajzpKPd0XjSb94z/+I5D1I4259tprgazvZCxiv8IKKwBwwQUXANnspzZSVTPCKqr1xBNPAOlsOU1UtN92bPmyMwyW3Z9BkvpOGusRRuwVGdffsK9w+L/GDanfeN7xP6HY+af7zV577ZVrO4Ng5syZQJYFKozYd1tmWl/R424pA1gTxK45ee8Pb775JpDNz6BIfaxOL730UqAZ2Z5SykbC1YoXK8O//du/BWDPPffMtf2qZycWR+zNzMzMzGqgq4h90T5kitSHuWnD5aZMmQJk+V5lEPo4jpZY2ev17bbbru37KkONvFf/1XB9zdaoSLxyQY8fP77j5+fdzybSLI4rr7xyruWV0Uh9Iv/Df/gPbdf/+c9/Djhi/+/ljcjkXU79xFdZZZWW1xU1e/vtt9tuN1Tn8yH1nbu9fh944IFdrS/h/qgFoNtW0H6k73Dvvfd2fD+vvDMq5z0WlMf71FNPBbJxGYM8FqXsNafb7xq2Klpc6vdTajbrboWZt6rmiL2ZmZmZWQ30JI99aP311wdGRupjT0NhpD5mEJ/me01lqjkCYtTnMoxAqh/sJZdckutzJKwLfb4yWDSxrsIyis0GnIp2XXzxxUDWahKKtc40Ubd9hou66KKLANh0002B+FwbTZAqe0XGh4eHR+VzU3V82mmntV2ujq0tP/nJT4Dsu6kFKtRta0VqvfB9ZU/TmKwm1EVVrYgSnk9hmSlj3Ycfftjx8/LuX53F7sV5zwudV+pjH9t+LOtTVRyxNzMzMzOrgVGJ2MeET4axTCp16OvYr8r2y0tl6TnnnHO627EBFjteNZtjLO98bL1YpD5cT2WvVpf58+cD2Uy0TY7EhPJGYHR+pMZFpDJb1LHsi34nXd91fCq6VTRKHFs+tf6hhx7a8v+SJUtKfX4/i30HXQseffRRIDuew0xFkpqLpqr5IZQf3EaqapyBMhatvfbaAKy66qrd7VgDpK4t/X49d8TezMzMzKwGejLzrP4/+eSTAdhpp506rq++lzH9/nQ0lrrNalBVH0bXUZzK5rDDDqtkO1I0YlnH/qpVCctGGYliEU054YQT2r7uMh1ZpuoLvHjxYiB+3d9xxx0BOOWUUzpuP1bGigJrbIr6FqvlKrWfg1h3qdnfNYOsykCtFal7b16pMV2iutAxkFKn1pS8qjr+tB1ltov1sbdM3vEPRe+lRfPVd3u8O2JvZmZmZlYDPe1jv+aaa7b8Hz6VxCIooUHOaTtWlIFlxowZQP68rVK0zDUzrfqRW3WKHvdXX301kNVJuB2fT9177rnnAHj11VfHeE/6T7fRJvUDf/zxxzsuN2fOnJb/NWupZqxduHBh2/XqfNyncqOrbu66666W/2PbyXutSI2pSmUbSalzncWULbOi9/rUepZf2MI7Vi1NjtibmZmZmdVAqYh96snxyCOPBLKZZkOarbHJmVO6lXoSPOmkk1r+xupCwhnQFA1bccUVW16fNm1a2/WvvPJKAPbYYw8APvroo46f18TowA033ADAggULWl4vO/uc1lNdaRbhJpbtaHvqqaeAdNYQ18VIectEs5DGzJ07t6vPq0Pf+qLyRvRj73fL50VxsXt9apzCtddeC2StM+Kyr15YprE89qn1quKIvZmZmZlZDfSkj30qmjx9+nQge1pRBgrLr+gMm0VnUlMf/dAmm2zS9nXNbGhpVc86p7pyJKZ7KkP9zTurb5OydqSkrjVVl1XZ476O50vRiHusLopm8UipY1mPleuuuw6A8ePHt31/5513BkbOC+TWxeoUHRsSlvGll15ayfZjHLE3MzMzM6uBoeHhYYaGhoaLrJSKCmy++eZAvK/9s88+C8CZZ56Za3u9Njw8XE0y3wrkrYu8T9lVRcd6lf8+NIh1EaoqApK3taVX50sd6iIlVYbz5s1r+/q+++4LwN57791x+1XVTR3rYqyv+2XVsS4GVZPrIjYvisZydTvHTVFNrIvw/nHAAQe0vB+OUZR77rkHgHfeeSfXdotyxN7MzMzMrAZK9bFP9c9Wjuef/vSnABx88MEt7ytTy5NPPlnm443qZipz/9Teqaq1xHmHR08YKVHromiGTc1q+uCDD47eztVc1f26zQZR3uu9sqDlXd+6lyrbcO6Y2267rZe7E+WIvZmZmZlZDZTqY5/XaPfxKquJfcP6leuifzShLno1BqVqTaiLQeG66B91rItBuSaF6lgXvVLVjM8xjtibmZmZmdXA0PBwXz/YmJmZmZlZDo7Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVgH/Ym5mZmZnVwLIAQ0NDw738kG984xttX//Vr37Vy4/NbXh4eGis90F6XRf9znXRP1wX/cN10T9cF/3DddE/XBf9wxF7MzMzM7MaGBoeHi79dBOLxPdKryL8g/CkmbesR6sVJNyfqj53EOoipex5oTIsun6Tz4umcF30D9dF3Gjfp+pYFyrDVBn16h5cVh3rYlA5Ym9mZmZmVgOVRuzzPmEWfbLsNgKaMshPmmWju/3WAiCDXBcpqTLPW9ZVbSelznUxaFwX/aOJdVH23t1rdayLvGMSu+0xUXVd1rEuBpUj9mZmZmZmNVBJxL7qCHzVT6ax7cogPWlW1f+61+MjykYDBqkuigrPl9FqwXJdZEartaNqdaiLoq2E/VpXdaiLqo1V5rs61IXHKlavV+dFv2d4FEfszczMzMxqYEwi9hMnTiy0/Jw5c1r+f/fddwG46aabWl5PRacHOWI/Wk/1sdaSov2/mxwljkXmpdun+7yRTNdFXK/OJ0fD4sqO66lqHJHHnuTXr2OwQnWoi9hxmooOV30N67Yu61AXRfXrfcQRezMzMzOzGuhJHvvtttsOgDXXXBOAadOmAbDNNtsA8Pjjj7ddr2h/8KuvvhqA+fPnA/DJJ5903N86Rexj3+WnP/0pAMstt1zL66qDkFpDYu/LZ5991vH9qgxCXVRlmWWWafk/bJlK1cnJJ58MwMKFCzsu1+SIfdmobb/1765TXZQtu36JItehLnpF1zRdy1555RUgfo1ylLh4xH6NNdYAYNKkSUB2n0jdy32/6F7qflJ2DoK88taNI/ZmZmZmZjWwbC82euqppwLZ0/vqq6/ei49hpZVWAmDu3Lk92X4/Sj2xTZ8+ve3rqTq48847O75/4YUXAvDoo492XK6JUn3qRe8fcMABANx6660d10vVmcaYDNoMhf2kbOQkb5TNZZ1fquy22mqrtuu99tprACxevDjXdiy/2Pmhe/tdd90FwCWXXALARhtt1LLeokWLADjxxBM7bt91lLbaaqsBcOCBBwKwdOlSYOS9O3Yv32STTYB4XdhIvWrxTXEfezMzMzMz603EPpT36UXLKRJ5yCGHtLx/2223AVmk3tKqysAS1qH63O+77749+dxBkvqu48aNA7LWlLAsY+fHe++91/b1FVZYAcjOj1VWWQXIxpyktmsjy2b55ZcHsrKNWbJkCTByzIkjj2mpMVQqe9VFSGO3wvXU8vWLX/wCgI8++qjtcnn3p8lSx7Ei9WuttRYAN954I5Bdg8Ky/vDDD0t9rltdRn7nv//7vwdghx126LhczHHHHQfA+PHjAfjggw9KbadOit4jUy1YutcXpfuJ7i/d3k8csTczMzMzq4FKI/Z6ujjqqKMA2G+//Vrejz3tHHvssQD8+Mc/bnld2W5E/fluueUWAFZeeWUgixoceuihbT+niU+iefUqqtvECGasLHW8hmNBUn3ydTyH9t57bwCOOeYYAPbcc08gi8RcfPHFRXa71vLmfD7iiCOArGwlnCvjhRdeAOKZJRwdHinvteB73/seAJtvvjmQXdd//vOft11e21OfY90PfvKTn3Rc3uJSZbTpppsCcN555wEwb968tss99thjAMyePbvU53Y7n0qdKPtNGKkvSte422+/HSg/K32d6iA1Z0bR76prloS9TiS8r/z2t78F0vfuvPvniL2ZmZmZWQ1UErHP+1Rz0kknAXDZZZe1vD5lypS2y4d9jMM+YUNDf0qbqv59MXV42i+672WfxmPbcX/tuFTZnHPOOUCW1SPMMHTGGWe0LK/+djovwv7cioKFMzhPnToVgBdffLHI7tdK0VznCxYsAGCnnXZqeV3Ca8uf//mfA1l/b0VYdt5551z7NcjXoLLy9psOxzfo/VS/VS13wgknAFlUOeSW3PxSfYlF92DZYIMNgCwbTmp7eeugCXUV+46pcT95j2uNXdHyyy77p59/N998c9vtNEF4Xc57nKl1fNasWUBWR6nfotp+uJzmeNL23njjDSDrc1+UI/ZmZmZmZjXQk6w466+/PjDyaSiW5UN951Ouueaaju8r8vnss8/m2l4dqe9W6smxKk2IpJSlsgmzebz11ltAdn6kjteirSZNrpO80WH14/6Hf/gHIIvYpyhPt3z3u98Fslkdn3rqKSDrgxxy1Hjkdz7ttNOAkS1QqVaO8P0jjzwSyF8Xlp/KeMMNN2z5X5QbPYzUp7Ynu+66KwDrrrsuADfccEPZXa2d1O8eUZmuuOKKAOyxxx4AHH744QBMnjwZyH4b7LXXXgB8+umnLdtRz4g6ttaX/S66XyiyrvNgiy22aLv98PjefvvtW5ZXXYgi/ltuuSUAb7/9dtvt5N1/R+zNzMzMzGqgq4h9LJLy5JNPAtnTu54y7rjjjo7b69WTYZOiYuoDHMsF/eCDDwIwbdo0ICubV155BcjfFzJVl02WOt50Xujvc889B8C1116ba7t5M02EmtDPO+81RJGXMEocbidVVorAqO+xomtlIy1NEJatyuxf/uVfWt4XRYP/7M/+DMjOl3B7ct999wEjr1F1Pu5TUi1FseNzl112AWCNNdYAYLfddgOy/PQ/+MEPgHikPtzuhAkTgCxCL8oAptlVlU0kHI8X2/86iF1zNJOsxpCob3zoy1/+MpDdR5Th65RTTgGysVdhnaisw/ESUseyztsasfHGGwNZa6DqIEa/r9Rqov/Var/22msD2bUpzBwZKjsGwBF7MzMzM7MaqKSPfeypRxknlNf++eef77id1FOUosx6gtUMg9dddx0A66yzToG9HmyxmfpiOZxl6623bvlfdaOn+qqezpvYlzj1HWMRGeVGV+RE4yS0nEbgK5p11VVXAVk0TfL21W+Csv1Dw+U32WQTIDtPwshiuPz1118PwC9/+UsA3n///Zb9aaKyrRXKBpW333ZMk8teimZJ07UlFSW+++67gezeHtueWsb+9//+30B2v4ltLxzL0mQzZ84EsjIOo7wauxibx0fcapiJ/X5STwfl/P/9738PZC1NMY888ggQb2ESXctmzJgBwNKlS9suF+a5L8oRezMzMzOzGugqYp96AlQ/opVWWgmAd955p9Tn6Glf/WJF/fzUr/zNN99seb+OkZqwzItmSlHrxrbbbtvVfqhf7Lvvvtv2c+pY9jGpaJhmxgxf13rqp/3aa68BWR9h9dtWHvzjjjuu7edqBL3681n8PFGZrbnmmm2XE/UlVgaJAw44AIDDDjus7fLhMaB8xGeeeWbRXa+doq0nl19+OZBljtB5oH6rt912G5DVSZOuNVWJ1YX6An/nO98BRpatosN6XXURo3u3+ih/9atf7bg/anV8+OGH2y5X57qOfTeNR4hF6nWe6HwI5/vRmJRwPJE+T8trhudvfetbpfZ/EIXngbLTaLzP9OnTgZF1o9ZEZd666KKLcn2etqNrms6Lqjlib2ZmZmZWA6M686zE+jeF1K9J/fJOP/30tsuF/ZHq2Jes7Hdab731gOxpXk+YVe2H+kK+/PLLbd+PqWPkJfbd1Z9O/e/UH0/9tEV1pTEkqbLU+4ouP/TQQyX2ulnOPvtsIJsFOHYN+qu/+isgay3Zf//9W5aPURRNs2wrYt+EjERVefzxx4GsJUt5tsOyc1nmFzv+wv91TYpF6hU1Dvtzh8vr3n300UcDWdY1UcRT44sUodd9JNy/Ot3T817X9d1XX331tsu9/vrrAKy88spttxvWyZe+9CUg6+kg6lGh9escsY9lg1KkXr8lv/a1r7Usr9Z0lbXGTqWEdaLWlzBSrzrROLt7770XyGaezTs/izhib2ZmZmZWAz2ZebZbeoIsO5NsHSM5eTOuhBQlVr/tql1yySVA9qSpJ9nh4eGW5epYJzGx76rXJ02aBGRRXUULJBXReeCBB4Asn30YcSm7f5ZRZD+cNThGGbrC/q6pMTFNrIvUd7711lsLLS9h2SqPdzgOqOh26yDvd/3iF7/Y8r+Oa62vSH24XZV96t6tSP0f/vAHIJt1OG8mlzq0fMWixuF31XiesG/9J598AmRlrHmDQuH2lFVQY1hiy2m7aq1sAvU8mDdvXsvripgff/zxhbYXlqnOCx3/orp8+umnAfjZz36Wa3spjtibmZmZmdVAT2aejcn71DFnzhwAfvOb3wDxWVStd/L2ydRy6pumyP3ixYvbLhca5MhLKG+uaEXoNWvjBRdc0HY5Ky+MJOr/vBH4opRt6m/+5m9aPtcyec/1Xt1Xym6/TmIRcM3XsP3227e8r+M61eKrMtV1P3bvVp965WTfZ599gJEtvOF2fT5l5s+fD8Dtt9/ecbmw7PS7KuxjH9Oke7b+hhH7gw46CICpU6cCWYQ9XD/v8an8+KK6TPXZL/o5jtibmZmZmdVAT/PYx6Se+E488UQg61umz1Gf5NTsXrH9q9OTZij2RKcZM8N+ejF58+SHT7rqO7bZZpsBWR7YMMtB7PPqWDdh2em43WCDDQC4+uqrW94vO44ipY5lGzNa3zWsi0svvTTXek2qi5iw7JStY+eddwayGctTlKnFRkpdK8aNGwfALbfc0vK6js8LL7wQgEcffTTX5x188MFANnOs5n8QzbER3rtj9+gm3LtjdaS5ATQPUOjHP/4xAPvuu2+p7VsmzIqj8QphNrS8rRyhsC7DFqyPP/4YyOYQCPcr/D/veeCIvZmZmZlZDVTSxz71NJE3CqwZ0JRZIlzu4osvBuB3v/tdx/2wkXWx7LJ/qmqVbWq9omWpmdSUe1oRGtdNRnnqRbmeRWU0fvx4IJvdVxlWZPfddwfg//2//wfAtddeW/3O1kR4HijjQ/h+1a0ldYww9opyRGusSWoWR/UV1jVGebd1DUppQjQ4NhZKwrIKr9MLFy4EYMMNN8z1eeFMzZrZXDM8f//732/Zfuq8q2Od5KUZlsNWj5TY7zH9H8uKk2olqXOruigiryxO+s5hpD52XsXKUC25mjdF1LJ1ww035Nq/omXviL2ZmZmZWQ1U2sc+b1RLkcr/83/+T8vrypEbzsiZdz+a9ISZEpshdq+99gKyCH7oscceA7J+4OGMgXkpgrP55psDMHfu3FLbGUSx8yA23kHLqz/eHnvsAWSR+xiVrVVHkUplAxFF0XRcW/VU9mGuZ9F50qRrSbdSrRM33nhj2/fV8nrqqae2XS91r1driuYSiOVat/w0O+/bb7/d9v3U77GhoaGO7+ve/+abb3bcfh1+V5XtQZBaT331v/e97wEj79GaRTs19rDbMnbE3szMzMysBlrCtqn+Qimxp4wzzzwTgF122QWAs846C4BTTjml7fLh5y5atAiA5557ruPn1umJsqxYZEVP+xKOqFf++SeeeKLl9bvuuguAKVOmtN1ualzF9ddf33H/mkDfWTMJhg4//HBg5AyZP/nJT4Asa8hOO+3U8v7EiROBbNyE+lDaSDoeH3nkESDr+6v83WpNUX5t9REWZfNwxL57RTOfpDJzhS1hzzzzDAA/+tGPAHjnnXdattOka1DqXq689Yo0hmOjROun8m2L6sCq8w//8A8A/PCHP2x5PfW7LXb/CZe/6qqrgJHZplL9ygdJt/ueWl/zNMRa05Xxa+bMmaU+Py9H7M3MzMzMaqAlYh+bZbTsaGmNvD/22GMBeOqpp4D8kXplBdEI4hhnXMmkyuIPf/gDkOVNVd/HVVddtWV9RSaVj77s57311luJPTZJZYhQxCXso//444+3/D/IEZVei2VcWWmllYCRkfqQ8huHWQ50rdLYFIuLXTO6bTGWhx56CMgi9eLzYSRd90XXEvWND/2v//W/gHjLlc6PWJ9610Gm6LiFlNjvNLVopTLi5d3eIKtqVuvYPTbWq0TU+yTv55Utc0fszczMzMxqIFdWHGWpUTabMFoVPlXMmDEDyJ7qFalP0XY082z4dHPccccBWd5jGyn1hPd3f/d3QJYLXSPlw/zeqsOynxPO4GZZVPe+++5reb1ovmLR072ia2EffUfui+s2WpzqD95kecs0tVxYlt2eP02sm7yRQd2Ldb9YunQpkI29Up9iteyq/3eTyzal6HmgMlx99dXbLhfrQRH+DguFOdrDbFThdt0zIn48K2tajMZonXTSSS2v96pMHbE3MzMzM6uBXBH7gw8+GMie2kPbbbddrg9LRQlSTy933HFH2/UsPQ5CWQ/0V+bNm9eyXN6ZOEPDw8MALF68GEhH6uvYfy9F59H+++8PwPLLLw9kERW1jJ122mm5tqcyi/WHbbJuIyFFI/eqS/XV1yza4f404TiPqap/q5x99tlA1oKs5ZXpSNHm2H40oS7yzjUTvh67Xyiqq7FayiKl637ZaHSTpa41YcT81ltvBbLWdmW0k/POOw/I5iIIffLJJwA8/fTTAPzsZz/r+Lmx/W2S2HceN24cALfcckvb98O5A8p+TlGO2JuZmZmZ1UDHiH2v+1Q1MWo72lSmmtUxll+127JXxEazB+fdrybRzLLKBX3MMccAIyMymu1RkZibb76543aV9aCJZRoq2j+7KI1FiUXZ5syZA2StM7H9c11VT2WqiH2TFT0PwuUVkdcspFpefeoVqc+b1z71+ZaViaK806dPB+CnP/1p2+XVSi433XQTkEWRY6ZOnQpk2dVi2aiaWEd5f/OutdZaAFxyySUt66kOUr+Dev3b2hF7MzMzM7MaGBoeHmZoaGgY4k9q66+/PgCXX355243E1os9lWgm2pTf/e53Lf8r4lm14eHhYh2hekh1UVTqKXvXXXcFRo7K7tbLL78MxGdSK9pPrw51kdeLL74IwMYbb5xreUUDlAVE/8fOi24jL02qi5jw+NV4ojAzmJZTH+RwZuduo19NrItUVCusCy2vbGqxPvbSxPMibyt5eL/QjLTK7pE34tjrqO8g1kXe67LmLQlnb49JRdx33nlnIGt1TGUXLDHObuDqIiV2voTZBdt8fq7t9ur8cMTezMzMzKwG2vaxD59S1K8u9n74+vbbbw9kUSv1O9UspM8++2yunYuN6o59bhP7hOWl2RjHjx8PwJ133gnk71cXq3O1qjjHbSZvWagvcGr8g6jPveoonFkz5POhuKLXkrCuzz///ELrW3Eq27zZ2Lqdm6DOwjJ5/fXXAZgyZUrL6++9917L/z6+qxO75txwww0ALLvsn36m6XdUajux/9WSpUi96zAudq3Q7yZF6lWGKtPjjz++9zuXgyP2ZmZmZmY10DErTvhEpz6LqVm2FElZddVVAZg7d26u7YdPSYpkWnWee+45IB7Fij2pfvrppwAsWLAAyJ7+1fcypolRgVSEMCwTtUyl+tw3sSzHStE+xJdeeimQtYxZ76huwtmyU3z+pG2zzTYATJw4seV1zZodzm4tLtveUdnrHqxsNhK7VqlFV/f8Z555puN6rsO4I488EhjZkiXKCtgvHLE3MzMzM6uBXHns9SSnKK36F1U9M1neGdgsLvUUrjpUhDFvzmf1LXv44YdzLe+n/+JlMHv2bABmzZrV9n3VlfLWP/LII+V3zkoJo8SpMSmxVkmfH+Wp7DS7dZihyK0ncal7rCKSKkNFi2PHq4/j8vK2lquM1ede4yAkdg+/7rrrgHjPB9ddfrG60e+pcK6Zsf6t6oi9mZmZmVkN5Mpjn1dVsz6Odo7cOuRfLTp7XK+eKJ2ve6SydVM2m0dVMwjWsS5SUmU9b948YGSZ9jpK3OS6SOVaX3fddVtev+eee4B41iifF5lu7wNjHfWtQ11UNfPrWM8tUIe6iNH4hvvuu6/l9TvuuAOIjz0Jjdb54oi9mZmZmVkNtI3YhwbtybGoOjxplo3q9ps61EWoqropG8l3ZDK/VLTs4IMPBkZmLlIfy5deeqllOyHXRX69Or4dsa+POtRF2VaTfruH17kuNOPyrbfeCmRjrV555RUAZsyYkWv7jtibmZmZmVluLRH7pqrDk2ZduC4yVUd9i3Jd5OfWk/4TyxDmiH19uC76R53roqoxiY7Ym5mZmZlZbkPDw41+yDQzMzMzqwVH7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMasA/7M3MzMzMamBZgKGhoeGx3pGxNDw8PDTW+yCuC9dFv3Bd9A/XRf9wXfQP10X/cF30j2XH8sO/8Y1vAPCrX/1qLHdjIKnspGwZhtsJt1e2jqrav34wWmVt1YuVeVFV13m3222CqupOXNbFVX3NylunrqtM0fPAZWfuimNmZmZmVgNDw8PDY9ZsUTYaUHU0eBCbkPKWQVVlXHT9svqxLvKWYdHjst9brPqxLkKDGlEsWveDUBe90u15UvUxMoh1MVbnSdHPa+J5UVXdjPX9pA51UReO2JuZmZmZ1cCoROx7FQWr6gm1Tk+aYVkfeeSRAGy++eYtrx911FG5tnfNNde0/L9gwQIADj/88LK72NEg10XR47Hf+9wPcl3k1W998GPbcV1UzxH7dEtvr1umuv2cQayLUNkeCL0a++W6KK5f7tniiL2ZmZmZWQ2Mah/7sv22e/0UVYcnTQnL6s477wRgmWWWAbrPbiMPPvggAO+++y4ABx54YKHtxgxiXaiMVlttNQAOOeSQlvd33333ivdsdPRjXfT6WjBx4sSO78+ZM6ft68888wwAM2fObHk9jEiWjVD2Y130Wr9moxrEuhir/te9/txBrItQr8uo1+PxpA51kddYt/SmOGJvZmZmZlYDlUbsU0+GRZ9Mr7vuuravL168GMiixnnVuf9qWPa77bYbADNmzACq6ysZs88++7R8Ttlo2yDVhb7jGmusAWStFip7fecTTzwRgEWLFvVkP3ulH+si73F18MEHA1ldTJs2Dcjq6Oabb25ZfsqUKUA2JqWou+++G4BJkya1vP7JJ58AMH/+/Jb/Y5pwjYpdI1KWW245AObOnQtkrZAxn332WdFdzGUQ6yJvNFZleuyxxwLxa1aqDrfYYgsANthgAyA7/6SquhnEugjl7ckQHu8XX3wxkP0OGussOk2oi6LGav4fR+zNzMzMzGqg0plnu80GstFGGwFwySWX5NpOKmJfNjJUB4rUVyVvWdZpxtmY8Dtut912wMhIvcyePRvIosKff/55j/ewuRSp32uvvYBsvIOyON10001AFrE/4IADWpYra/LkyUD8/Hj00UcBeO+994B6nhd5Fe3Lu//++wOwzTbbAHDHHXfkWl/L3XDDDR33o8l1IWeffTYAW221FQAnnHACAJtuummp7R1xxBFANmZl9dVXb3lf58Ghhx5aavt1kpoP5ZVXXgHgxRdfbHn92Wef7bjdcDsaA6bzodcZkQZZ3u9+xhlndHx/hRVWAODDDz+s5PPycsTezMzMzKwGKonYF42Iq6/YuHHjgCyKNm/evFzrn3/++QCsvPLKALz//vsADA+371ZV5yfPWNmfc845QBaB0RPjRx991Hb5WMRSdaMnz6KtIHV6+i+b1WnJkiUA/OQnP+m4XNHthprQWiKx77brrrsC8eNZr+tv2UxcVZVtnc6PovJ+Z0XqZ82alWt9lan+/tu//RuQnYex5ZtYBzHK/qTWRgnLKHU/SJWpzkNF8GOaUDdFf0cpWqzxban11Zq53377AVnE/8wzz2xZrgllXdRhhx0GZGUXipW9yrZo60rs9bx144i9mZmZmVkNVBKxz5sJRe+r357+7rnnngDcddddQNYXOfZ0ovUU8T/uuOO6+wIDKPV0Hvb9euyxxwC46qqr2i6vrB0hRez33ntvAI455hggqxs9ifopPxPWjbIXPP74411tL1XGYx19roNBmf10kKW+81prrQVkLbOx1pe8dXXLLbcAWWSzCVIR9FTZKYtN2Me+qvNDdRq771j1VKeXX375GO9J/yp7PU6Nk+j154ccsTczMzMzq4GuIvZFn0ry5lZPPbWoj/4qq6xSaP+aEB3Tdxwa+lNKWUXu33zzTSDdlzHmvvvuA7J+qqecckrL+7GyrkP/1bzRLbU4hTQGJJXDXKouq1SUrk7nifLQH3/88QB88MEHHZdP1W3Y/1Q0hiXMqhOWnc43tXzZyGuCxkXo/qDsHbq+x47flVZaCYCTTjoJyPrgjx8/vmU51dUgH9dF5Y0gxl6P9bHP66ijjgKyukl9XhPlvTfGylLHdcrJJ58MwAsvvNDyurIQamziQQcdBGT3+G7npBlk4Xe+5pprgOy3Z97Wc13DNH40rDNldnzooYdK7VdsPxyxNzMzMzOrgUrz2EvsKSLMrZ73ibUOUd9eSZWJnhCL9rMOM0tI+NSf2o8m1JlaR6o2WtGtQayj2DUhfF3RW7Wa5KU6TX3O+uuvD8C2224LZPm6817TZBDroCq/+c1vgJEza4ZlcttttwFZBHPp0qUA3HnnnUCWiz22fhOFx5n+V2uHIvOaOVmeeuopIGsBu/baa3u6n9KkOiuaYSi1ndh6Z511FjAy4q9MdxKef75GZTTeZ8011wSy8yU1U7laRVR2ylIoX/nKV1r+zxu5T3HE3szMzMysBno68+yyy/5p85rFLkZPhjvttBMAb7/9NpBFEz799NOW5cLPUf/WsB9rk58wQ6nIZoryt+oJNKZJT/n6brFIY97IS9EyUl3svPPOHZdTZFMmTZoEwMKFC1teH8Q6S43hCF/XGJHll1++Zbl7770XyGa7Vl/KvDbccEOgfKQ+tr9NkDo/dJxqZmedZ+oDvPHGGwNZ1Cw8JrS+slI1UexatNxyywFZi1Y4Xm2LLbYA4NJLLwWyCH/ez9O4iaKaeB5I3vtF7LgO61rnxW9/+9uuPs/gySefbPlfZa2xXIrk65oULhezxhprANkMzJpv6Iknnmi7fN7fFo7Ym5mZmZnVQKV57EOK1CsaFvPGG28AsNlmmwFZ9OCZZ54Bsshk7HOanHGibI7yohER1YEyVjRR3qdlze77ta99DYB11lmnks+fPHkykM2ClxL2qVQ/2a233hrIzq9BFOs7LOHxrVl/w/fVqqjczmodDJeL1Xmq1UTyzrBZZ3kztYTLTZ06FYD/8l/+C5C1smhujRjlYM9bR3WOEpftvx2uL6mWpzAbVa9aL+sgVja63qvlVWXzu9/9Dhj5u0jzP2gso6LMut5XtZ9NqKO8PRwuu+wyACZMmACMnB17k002aflfc2qEdaJ7ulrSYhH72P6FHLE3MzMzM6uBnmTF0dNHKlKvTBUzZ84ERj7l77vvvgDcf//9HbeTiobV8Qmz2zzFse3Flo/l0s37+YNcB6kZXdV/W9RPTq8XjZLFlj/88MNb9ie13bDswwwXse30c9/Lqmb4k3D8TtHP1zUrdl5I2KrYz2XcL9TPW5m9Tj/9dAB+8YtftF1edaN+4cow4bKuTt6y1FiVF198se37Ref2qKNUi5Vax9UPW1S2am0cN24cAH/4wx9a1j/11FNb/g8/V/cTzVXQ5LqQWMtv6veL5gk68cQTOy6nsp44cWK5HczJEXszMzMzsxroScReYk85zz77LJD1AYtluNBIfEXku/3cJum2DJT9Rrlu1a9P1Dry0ksv9XQ/+kkswnL11Vd3XK/qMgj347PPPgOy2YDVUqZIZ1iXyvu9aNEiIIsydNsPt58UbSkqWkfHHXcckJWxsovEthPL3NVE4fH12muvAfDII48AI8fxrLfeekB6LgLdV37wgx8A+TO51FnVLaapa0Ts3q3Xw3t/bH9j262zVMtw6MYbbwRG/j5KXb8Vqb/hhhta/uZdv86KHmd5s6DpGqZrW7ieWvljGYxi2/XMs2ZmZmZmNVZJxD58wlMfsLDvsSgf8corrwzAPvvsA8CXvvQlAKZPn952u6IIpZ5Uwz74dejXHeq2n3bRaGw4PiIsywcffBCAm2++uav9HASp7zRt2jQA7rrrrkq3Kyp7jXNQHntRdpAf/vCHLX9FM3Jq1rs690Uu2wdfyykrwZ/92Z8BWc70cPvKhhCbrTHcD8+xEacsHprdUVFd1YWiv7G6ve6664AsChyLBjexzGOzmxZtBU9tVzS+LrxG5V2/DtegvLptIVX2wKrHHTXxPKlaWAe6X8R+E7/88stANjdBt3XoiL2ZmZmZWQ10FbGPPdnFIoui/MIaza3+qsoBuv3227ddL3yKUdQh7HuZmpVyEJV9uo9FbGK6zW5Th7Lullqi1AdSs8qFUlGrvFEttWA1MVJTVeQv3I6uRWEEpWgLgPvUjxS7lv3t3/4tkM0OrBYw5X6ObSevOt0PUorOJh07rtWqN1rqNM5ntKnMdM3SNaxJx32vFb03jxVH7M3MzMzMaqDSrDh6WtEo66VLlwIj+/OpX+raa68NwHnnnQdkeVdTlPd+8eLFHfcj/L9OT6xFc5m/8sorABx88MFA1kde/aslnCkttj3163733Xdb9sdgaGgIyI57/dXrKbGyDM+rW2+9FcjOp9tuuw2AAw44oGW9M888E8j6Mu+222659mOQxc4PlVEsEqnWlv/wH/4DkI1PCIV964tqYvaPVBRZ7ytDRDivSUx4n0m1ktTxfpBX0YiiWht1zVAWtNi9N/yc8DzU+ImUJtZNTN57vZb7whf+FK/VeaC++Na91HGpXiiaBVjZ0n7+858DI+tCv8NUZ3nrOMURezMzMzOzGuhJHns9day//voAbLvttgA89dRTLcsp6qUZBlOUM1ojiCXWp75f+jtVKfbEpswRc+bMaflffvSjHwHZjGdf+cpXAFh33XUB+OIXvwjExzeElMFCmhCBjB1neaO3w8PDQNYnXutrhmVJHbfnnntu2/254447gJF5wJWpItYaU8cIZqrPsDIEpcSWy3uNURR5/vz5QBZFqzqTxSAoenzlbeHSGC1RRiPNsGzl6Vqiv7pffOtb32q7vK6FYV2fccYZQHbtiqnjtSil6u+qeUuse3mvy8pT//HHHwPZ7yhl9orReIjwN0C3HLE3MzMzM6uBnuSxlw033BCA1VdfvWW5vNGqTz/9FIDLL78ciOcnju1Pk5769WSoPl2hE044ARiZE11/U2VVtC5SdTxIdZM3e82FF14IZDNoxiiqNWXKFCAb/xCjOtL8EMoSsvXWW7d8vvrvpXJIK7L/9a9/HYCHH3647fcZBLFWuvA7TJo0CYAJEya0LBfT6zJoQoQ+lPf6v8suuwCwxhpr5NpeSLM3HnnkkUA8cj/I16S8YlnRvvnNbwJZXu3f//73QDaHTKwsdI0J83Gnjue33nqrwF43S+y80Jiq66+/vu16b775JgBXXnklAGeddRaQzTAe1onGJmq7ea+dlqY6mDdvXq7lVdapVsmydeKIvZmZmZlZDVQSsY9FYJTHO+zzm9eCBQuA7EmzyaqK8BWd7VQU5dXTfmp7dYxIpr7To48+CsCiRYtaXte4hpjY+6qDk046qWU5ReqLuvvuuwHYbLPNgCxSH35eHYSRjm9/+9tA/gwRRSMlseWU4eW//bf/BmQta8om1USp80jHd9hX+KOPPgKymWZj29H6umY1WayMVJZf+9rXANh4440B+Ld/+zcgfc0q+rnOopaWitxrrJTMnj0byDIVqU4VsQ/pGhSOUazjvbpbea9Ral187733Om4nbBWJtZqEyp4njtibmZmZmdVAT7LihP24y4qtH3vKaXIUQNk29txzTyAemcyb9z4sS/XbVn+/ZZf906EzdepUAD755BMgy/6h/6WOdRPrm7jBBhu0LLfrrrsCWUYJ9ZWPZZBIfV7o7bffBuDxxx9veV2fI8ohHUbq6yA8rpWPXnmFw+wEYd0tXLgQyPo8br755m23W5SiZ3/84x+BkS1esf2vo7xjq5Q9ShQN0/oaR6SxKcpIEWanWnPNNYH8rYxNEtaFor7KYKe/ErsXh9eYvHwPTwuznWmcm65Nus+o7sL1LK1oWSmDo8bx6Dw55ZRTOm5Pr+t8Uet5aj8csTczMzMza7BCEfu8TxXqC6ac6il33XUXkD8feCzy04Sn/bI5+lN1V7Tv/Z133tnyuqJqyp+ft8/ZIAnLKjbDcbh8OLuvIoiKPM6aNaur/TrwwAOBkVGAqnPj9qPY8asorjIIxSjadcwxxwBZ5OX111/P9fnKTCHKulPUIJ4Po+Wf/umfgGzWYAlnptX/GpOiTEiaPVgzMNd5npOUVIYvzQIfWy8sM11j8mYDsbhY64XKXNd3/Q2XT/1+cmaikfK2Iup+8t3vfhfIxn+m1pPLLrsMyGaa7TVH7M3MzMzMaqBQxL7obImpyMihhx4KpPuFF92vOur2O8bqouqolfr6p/ajDrrN//v+++8DWSQxJVZXeed3CMVaIOpQR5rdVy1H6nMf5g1WP+1w7oFYGSxZsqRl+5oNW8IZmfOqU9l3a9q0aUA2Z4ay4yjLmu4bojLTWJa86lzWVc9s3KvWDR/3aamyUWtJrI4eeOABIMuiE9t+E1uwwuMvVtbqhRJG6lNOO+00IBvDJb0+3h2xNzMzMzOrgUpnnk3l4ozNtqWIvZV3yCGHdHy/bP76qtRplsdUJoeqxjN0u1+jvf5YCsv2hRdeALJWEfW3Vqtg2Tp44403gCwrwnbbbdfyvrJCWf+pOvPEIEm1LuYtg6IzNjcxClxW2dYLl3F+o9VCdOyxxwLw4x//uKefE+OIvZmZmZlZDZSK2MeeEJWVIJzxT/2u99lnn0LbVQ7or371qy3vNyHCElP26TwWLY7171YmiRStrz7HU6ZMAbJMR02MJqQyT/RK2VaROp1P4XeJtWSlIov6Pzw/NGujZpQNafbHmDq1XPXKX//1XwOw4oorArDbbru1vB+r01i+ev2N3X+adF/p1VgtaeL1vltF7xc6H2bMmNFxu8rAcvPNN3f8XB//mdh8KOE1SFTGqd+2o80RezMzMzOzGqh05llFWMLsNttvvz0Azz//PADPPfccANOnTwey3Od5M7c06QkzL7WW6O/HH38MZBkmQt///veBLKtHWMbKu6on1pBy4mo201AsMtnkiE6sDPKWjY/3uFQrojKpaFbTVJnr+I8d36Gq55dogtTx/LOf/QzI5sxIlZUyV9jg8T09EzvOJ0+eDMRnARZFkXffffdc27WsbPRbVHMwac6AWMbG2Hw9eT+vV8e7I/ZmZmZmZjVQacReMwO++uqrAGyxxRYATJw4EYAjjzwSgMcffxxIR+bD7DmWCSOEn3zyCQBHH310rvWV+zz2FN+rGdIckUlnj7L8Uq0fOi/0v65Rq622GjDyOF+0aFHb1/MqO5NzE6WiVhq3c+GFF7ZdL0VzClxwwQVAliHJyisa9VXdatzdWI0/qgP9jgrdfvvtAPzgBz8Asp4RoSZfa0Kx42255ZYDslnhY+N5Zs6cCYwc15P6vFS+/Ko4Ym9mZmZmVgNDw8PDDA0NDXezkfBpZPXVVwdgs802K7Sda665puV/9a18+OGHW16v+mlneHi4b5oGytZF2UhKt7lzY9spu9061EVd1Kkuuo0IjnW0q051IXnHmkyYMAGAWbNm5dqu+iArcvnOO+90tZ+hOtZFXrHr/MknnwzAmmuuCYzsB+66yC92rdpll10AuOeee1peV4auJ554ooqPL22Q6iJWxupLr1muQzruN9lkEwA22GCDjtuLrd9rjtibmZmZmdVAJRH70GhHj7s1CE+aecum2zz3VW23bB0OQl00xSDURey8iEUW8xrrCH1oEOqiqFTdSdGZnXutjnUxVrq957su4kb7PBmkukgdd4sXLwbgxBNPBEb2JtF61157ba79Ge37iSP2ZmZmZmY1UCpi3y8j2Kt6ChqkJ81+VVVri+uifwxCXYx19Ha0DEJd9Kuq+7+6LuLKtpQ5Yp/p9e8rR+wzeeeOGbReKI7Ym5mZmZnVwKj2se/XKNogPmnWleuif7gu+ofron+4LvqH66J/DEJd9Fv2ml5xxN7MzMzMrAaGhocb/ZBpZmZmZlYLjtibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdXAsgBDQ0PDY70jY2l4eHhorPdBXBeui37huugfrov+4broH66L/uG66B+O2JuZmZmZ1cCy3az8jW98I9dyv/rVr1qW1/+x7cXet+rkrbsY19HoSdWV68LqIHacx+4fqeWtOnnv3TGuk/zCssxbdmXv6a6b+nHE3szMzMysBoaGh4dL90dKRVCKPgmO1ROn+4blV7SVpijXRdponSeui/7RhLooG/Ud7WhxE+oiFLu3j3XLb53romjZjnXkvc51MWgcsTczMzMzq4Gu+thL2af2bp/2LT+X9eAoO3YltZxZP0sdp3n72KfWs/7RbSv/IEp956oj9b4/5DfWLVBVccTezMzMzKwGetLHPnxqmThxIgBz5swptZPPPPMMAA8//HCu5Yv2+W9C37CikZHx48cDsNpqqwFZ3eWtC/exr07eiGPV4x/qWBdlo77dcl/itFQ/7uWXXx6AI444ouV/0TVJ16gUX6PyS90/br31ViC7X4QWLVoEwN13393xc5owDqhoNqfY8irrBQsWtH1fZT5aBrEuulXV/aPqSL8j9mZmZmZmNdBVH/vUU8auu+4KwEknnQTA0qVLgeJ9Hl9++WUApk6dCsDixYsBePDBB9suH9t+E/vzxaSiBrNmzQJgq622ArK6u+eeewBYaaWVeryHzZE6LlPH6TLLLANk58cnn3wCwPz581v+z/t5dZT3muP+qv1rhRVWAGDvvfcGRpb1X//1XwP5I/aWX+q4PuCAAzqu9/jjj1e+T3UTu0bp/xNOOAGALbfcEoB1110XgP3226/t9vbdd18APvvss6p31brU6/uEI/ZmZmZmZjVQKmLfbT/Vok8rkydPbvn/X//1X9tuJ9yPcD8dTctfBoqOpbbjbDu9F4vk7LjjjgCccsopLa+/9957QNaqov+brOrZG2PH/6Dlnh4kYeTxww8/BLJr1fe//30AHn30UcDXpn6gOlAri6LIoTqfB1W18h199NEAbLzxxrm2o4j+aPe172dlxzfE3i+b0ajXs2Y7Ym9mZmZmVgMdI/a9jngMD/9p4LL6zIcUiQmzH2y00UYA3HjjjQA88cQTALzxxhtAvE9Zk/oWx+ou9QSqMl1llVVyfU6dy3C0FO33HY5dibnpppuAZkQuu/2OusakWqpCqptDDjmk5XVdg5YsWdLVfjVRaozJz3/+cwDOPvtsIBsHpJar5ZZbru161ntz584FsjqQO+64A3Cd/HupstDvo7xirYcPPPAAAF/96lcLba+OYhH2svcPXfd1r837u+mjjz4CslbHqjlib2ZmZmZWAx0j9nnzqsaegmbPng1keezD5RWpP/TQQ4GRTz/ql3fMMce03f68efMAmDRpUst2in4fi/OsjYOvCeMhus2Ade+99wLZNUd0LQrtueeeLf+HEXvZZ599On6uxRW99mj5TTbZBIANNtigku02UdHz56CDDgKy+U/qfK3plSuvvBLIH/Wtis+HOJWJsj6prMLrfXg/kNh94bnnngPg1FNPbXm9qvEYjtibmZmZmdVAoaw4qacJvX/ZZZcBIyP14XKnnXYaAFOmTAFGZu9YZ511AHjyySeBrG+xhNGw8Olo5ZVXBuD999/vuN9NUHV/Puud8Dy77bbbALj00ksBmDBhQtvlwjoOW8DqoGwkMBaVUoaJMFIvRceaOFJZXrcRQ62v86RXn9MERVtJvvCFP8UIf/rTnxZavwlSZXHeeecBcOyxx1ayvdT4OouXxbhx4wC45ZZbADjnnHOALMNQeD9QXcTuE7G6evfdd4Hst/KMGTPy7XhOjtibmZmZmdVAqTz2sacQPX3E+jaGyy1cuLDt+3qaevPNN1v+/tM//RMAr7/+OgArrrgiAAceeGDb7WgkvkeDx51xxhm5lrv22msBeOihhzou5/56abFIeyyKoONc2T9S241F6OtQJ3mjVWGZhuttvvnmABx55JGFPv/EE08EsmvQ4YcfDsCcOXNyre/IflzeTF7q76oomvgaVZ3U7O2aI0PHvfrWV/U5TRL2s0555513gCzSr7GMRTW5zGW77bYDsjrQLO7K7hTec6u6bq+22mpAdq2qOnLviL2ZmZmZWQ20ROxTM8rGnvCUlSYVqb/mmmsAePDBB9tuL/U09PbbbwPZ05T6NcUi9rvvvjsA119/PZBF8KUJT6yx76gn1FmzZuVaT6O4YxyBzC81i11Ylurnl4rYi+pW0Yg6KTuzq65RylOvcT2xcUC6RilCL+EsjuGsv6I6yztjoaXvBxozogwUYb/W1DUq9jk2UqqMNO7n1ltv7bic6tDZoTIqk7zXc9G1R79/YjPKhveRoaGhjvtR5/Mhdb9Yc801gSxSr7II52FIbf/qq68G4OKLL255X7+vNC4utMYaawDp387h50ms7hyxNzMzMzOrgbZ97ItGxR5//PGO7++2225Auu9j2VzUipqp32xIs6mmPr+OYk/lekIs2zfS8subm1bLaRZUZYOKra/1FLlRRgpFgn75y192sdeDJXZt2GyzzYBsll5F7GNletdddwFZf23lRNf2db6oj2Qs4m/lhXWjstZ4CI25Cqm/t1pqLb+8c9OoLv7+7/8+13Z174+pc7Q4lPd3lbL4KXOKKIPXXnvtBeQfH2dx06ZNA2D11VfvuFxYd2FricY56H4Rvq7IvDI7br311i3bTbXwxvYjxhF7MzMzM7MaKDXzrPz85z/v+P75558PwAknnACUz1aQ2o8tttgCiOdiV99+9ZdtUpQgVHZmM/Uhi43Ab3KZpqRmcA7rRJF2tTTFcuRqvc8++wyAv/u7v2v7fioTSx3qTt9NkRGNu0lFDFV2KoN9990XyPpciv5XZGW99dZrWa8J/VWrlrdFVv1T//jHPwJZq0tI1/86H+e9kneW+bAuUuspghmOVUl9bp3EjsfFixcD2TVINP9OuL4i9eH6KkONPZk6dWrLcr2ao6YJ1zx9R2U/u+GGGzou/9JLL7Wsp54RKiP9Fg1besM6j+1HXo7Ym5mZmZnVQKk89ookxrLRyAUXXADAzjvv3PJ63lzTRcWyh2gmwib2sY/J+xSvunnggQd6uTu1lvdpW5HIiy66CBgZNY5t77XXXmu7XB1zpqfG4Vx33XUATJ48ue3yL7/8MgAzZ85sef2www4DsuM8jPTXOSrVL7qdVdjXqPLqdI3od+G15Morryy1nXC9HXfcsdR+lG3FH6RrYuy+kfodpPkali5dmutzUq2QGqMSUguwflsfeuihbbfjPvZmZmZmZg3SErFPPYF94Qt/eg5QPuHYesoR/fnnn7d9v+yTXt4+w+HrqX5+dRYra40GP+KIIzoup3zFqew5TehvV1YqyqycubfffjsA//W//te22wnXU97u2MyFefsw10lqBtgwUi/KuLLDDju0fT9vpCQcN6HMRjFNqJOiYveLefPmdVzv448/7vi+r1Fxea9RahVP1YV8+OGHHd93neRXNsIeG5PShFaa1HGVul/o989BBx0EZL9tJZzlXePhwtenT58OwIIFC4CRZR/+P3/+/JbtuI+9mZmZmVkD5epjr6cFZYyQbp+yi0bBYv9rPzTTZrhfsT72TXhiTUnV4VFHHQXAkiVLgHjZuywzeVuWRNkM9t9//47LaXtf/vKXgXiGIotHAm+55RZgZCaKWKQ+RjNuql/rWmutVWo/LRO7FqXydas1MXaNspGKRso126nOn5THHnsM8DUKRt4HUlmbiv4uStWhWudfffXVSrdbByobzV8So7kFQuoTH9I9XdRXv6iydeGIvZmZmZlZDZTKilPVk1xqO7F+f0U/P8xj3wSpPpN5XXbZZQDceeedHbdvmaJlc8wxxwAjowZhnT344INANitqqv9qnVtTYt9JrXKavVfjgVQW48aNa7ue3lcZpyIsxx9/PDAyy4FmuNV4JMsvrFNlJpoxYwYQP68++OCDlv/z5mRvsrz3B50/e+yxBzDy/ImtpwxFea9RTZB3PpOiv4u69a1vfQtIt8Y88sgjQJa1bZDEjlN99/feew8Yeb+QovfScBxqt4r+9vXdx8zMzMysBgpF7IeGhoD4U4uiXcq0okh53pkAy0ZUDjjgAADWXXfdtu+rn3jZfk6DIG/fd+X3VlaD1BOg6ny0owiDLO9xrDI7/fTTO76v7SlqFvYPz/s5dRSWkWZjvP/++4GsNUTXIl0Dzj33XACefvrplu2pbFPjJDTOIdwPRX5SMxRaWmo2RtE1KqXO50GvnHbaaQBcfvnluZZXpq689xcbqej9I0UZv373u9+1vK5rYmy+lNBOO+0EwLLL/uln43nnnZdrvX6Sul/ou+maEv5mzFvmZeswtV7eTHeO2JuZmZmZ1UCuiL2eCqZOndpxOfVv1ZNguH7ez5FUnnrlDNUI5Fi/pibksc9bxhMnTuy4fFjm119/fdvXy35+HRV9OldU4Dvf+Q4AkyZNyrUd5dxVhorU5zRRrJVDmVPU914ZtJ544omO2yt73OdtpWyybseAbLDBBgAce+yxQP6osmViZb/ffvsBsNFGG+Va76WXXgJgiy22KPS5TTgfqr53hn3ydU3TfCbKIjVhwgQgq5OwbvJG6kPbbrttqfX6UXgN+sUvfgFkvU6OO+64XNtZY401ADjllFPavv/OO+8AsPrqq7d8bkh568M8+KHUMeWIvZmZmZlZDbSN2BfNwx1bPyXcfmqG2nC5zTbbDMj6jcdotq/Y59dRtxkglLf1lVdeqWJ37N9RX8Vddtml43JvvPEGAB999BGQRRGc3SN9rQivLT/5yU/abqeqstR29t57b2DknB8WV/Y+o5bYWH7wOl/fu5Uqa+XnVpan0C9/+UsArrvuOgDuu+++CveuXspmaUq9r4xc6qmg8UTKJlV0f4oapPMrdb8Q3Wt1v9A1JrXeokWLAPjv//2/t/wvYcS+W6myd8TezMzMzKwGWiL2qX6hqT72RaVmlA0/X/2YlCM39WSq5e64446226tj5DP2ndS/7oorrgCykfIxF198MQALFy5s+/4gPa2PlVgZqW9w6vhbe+21K9+nuuq2lTEvbV+ZttQXWf27NcdALENXk/oW51W2D3I4Xih1DLjMqzsvlJ8+jNRXndGlDrqNzOverfuG6BpTNkKvftzKZhiOjYzZaqutci3Xz7q9DsfqLDaOM5wHKKRsalWdF47Ym5mZmZnVQKE89opSHX744W3fD0fQv/nmmwC8++67La+vsMIKANx+++1FPp4DDzwQSD+hyj//8z8D1eXLH2Tqn/3iiy+2fd9ZPLqXKivNoJk6fk8++eSuPqeJ8vahlLzR3Nh5oYxcolmDlcdbf0erJaGOhoeHO74/e/ZsAP7pn/4JyKJiLuPydJxqbphQWLaHHnpox+24LjJ5r0n6HaXfSbNmzQKyzF7dUnRYudtvvvlmoFlzbxQdzxkbx5OiugwzEMW2c9ttt3X8/NS4VHHE3szMzMysBgpF7FPCPPKPPfYYAH/1V3/V8r76dml2upS8T0megbA6sRk4rTjN0Kc8wyka32C9UzSiL2effTYwsp+p+qfWea6MqqWu659++ikAS5YsAWDcuHFt11dmlhhfu/I77LDDgGzsSNEIZaysXQfpe6le32GHHYCsb30sq1/R7Yv61CtSX1adfxuUjeiHLrnkko7rl/38FEfszczMzMxqoFTEPvWUEr6vnLjyta99LdfnVB2pbzKNj4jlJRbN4qgc6nV8Gu9W3uPyyiuvBLIyjVEZP/vss20/J9X/23VUndQ1TTPWluW6Gil2Pzn66KNb/lcLb9l5VVz26Xu3Wp6U7SYWafQ9t7jY8Rf+Pgpbprrdfkze+4uNVPW15JxzzmnZbrdjsxyxNzMzMzOrgbYR+9jTyOeffw5kI+Y1gjdG21EGiaqeELuNFtQxgpMqO0Viwqw44XqLFy8GsiwfNlLquNFYkryRevV5vOyyywp9Tp2O336TipSEr4czDcaWd50Vp36qEtbNtGnTgOz+ZHF577Hh8apr1D777JNruz7OM6myCDNspaisf/jDHwIwc+bMtsvlzcIWqzvXYX6xslIkPpX7/4wzzgDgzDPPbLs997E3MzMzM2ugthH71NP30qVLAdh3331bXlcWg6JiUYR33nkHgDXXXBPIMrWU3V4TxPpo6e+UKVOAbOZZva7ctspYZNWJPW3ffffdQHzGP88tMPbKlrXzeOcXu98o93M4P4poJk5H6vPLm50jXE73h7x83FdPZbrssn/62absabGy/vKXvwxkv6Nk/fXXB0bOLxR+ju8zaUXzy8ekeqEU3a4j9mZmZmZmNVCoj30ojKCrz/1qq63Wcb0VV1yx5X/13wvtvvvuufYjpklPnHn7ZIUzZMpHH33U8n/RPKtNVLa/6h133AHEZ/pzf9WxF6tb9YWMiZ2HjmAWp/tLbG6A1LgGK+76668H4MILL2x5PVbWblUsT3MGhL+HYtSnfu+99way302xVpgnn3yykv20TGrMlWYLnjRpEgATJkzouF44X1BVHLE3MzMzM6uBXH3sU/TEqP7ZsShVr57iHQ1Ly1tGqdHblqbWD7WK6Cl+k002AWCzzTYDfNwOEl27YlkOLr300tHepdrImyUt7/3DUeT8wnu1xv3oWiXh/9Y99XEPs6HllTdrWtH5UHxfKm/55ZcH4Nvf/jaQP+NR1WXviL2ZmZmZWQ20ROzLPtHF/q8qQpL3KcYRmerL3mVa3IcffgjAqaeeOsZ7YlXpNpLi86i4VJl5HFD3ykZrXbb5VR0Bj/WpDzPgpX6nxbbnuu2e5rJJeeqpp4Dqf7c5Ym9mZmZmVgNDw8PDDA0NDY/1jvx7ZZ9wyz7tDA8PdzeVbYV6XRexfqv9kru2znVRduzKWKlzXcSkzoODDz4YgI033rjl9ZtvvhmAl156qdR2U5pYFzLa94OUJtVFv0fwm1QXMalI/Gjd25tQF3nLctasWQCMHz8eyLKpaYxW6n7RLUfszczMzMxqoC8j9qOtCU+ag8J10T9cF/n1OrLpuugfrov+4broH66L7lXVuuKIvZmZmZlZDQwNDw/kg42ZmZmZmf07jtibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdWAf9ibmZmZmdXAsgBDQ0PDY70jY2l4eHhorPdBXBeui37hush84xvfaPv6r371q1H5fNdF/3Bd9A/XRf9wXfQPR+zNzMzMzGpg2V5sNIxuxaJasShYXqMVLRtEecs2VYap7YTrV/W5TTRaUeGxjj6PpaLHc7fLWe9UVZdmg8z33Ork/e2aWi+l13XhiL2ZmZmZWQ0MDQ8P96w/kp5i8kZ1u40ep8S2X+e+YWEdlI1y5a2zsnWk7dSxLkar9aTs9mPnaZPrIqaqFq6i52Ed6qLXLUXd1m2oifeLQdPEuohdr1PLp3R7HtahLmIR+7KR/LHiiL2ZmZmZWQ30NGIfU3XUuOh2QnV80ozp1RNoVXUziHVRVb+8sG7yRndTytbtINZFqOq6KatolC00iHVR9jtXHXmXqqJsg1gXKWNV5t224tSxLqpStPXQEfu4VBmljuOqyjgvR+zNzMzMzGqgUMS+6NN17Cll4sSJRfbx/zdnzhwA3n33XQBuuummXPuRUocnzbKRjwceeCDX9qdNm9byv+oitdyiRYta/k89uQ5iXeTtlxfaeuutAbjllls6Lhcra1EZ33333R2XC/dPmtDHvuh4n5DWGz9+PACrrbZay/upOop5+OGHAXjmmWc6fu4g1kXZ8QZlr+NVt3SFBrkuyup167qjxCNV9Tsr73ZTmth6EiurXXbZBYCrrrqq0PZ0f9D1/uyzz871uWXPD0fszczMzMxqoFAe+6JPD1r+hBNOAODJJ58E4PHHH++4vdST5ZVXXgnAfffd1/L6wQcfDMDixYvb7odlJk2aBMBuu+2Wa/lXXnkFyMpy6dKlwMhowezZs1vW23fffQH47LPPWpZrou222w6ANddcE8jKbtttt+24nso65v/+3/8LZOeDynrq1KkAfPLJJ23XW2aZZYBm1EnqO6oVccsttwSylidFWlS255xzDgDrrbcekL9vZczmm28OZOfNwoULOy4/CMq2Hm6xxRZAdtyGrX+hsJVEy+u41nZiHnzwQSB+vxjtfrH9JG90t9dR4Caqqmx0Hkh4Pui413kQfn6vxl30k7zfUffu73znOy1/89aVfmfper/uuusC8NZbbwFw5plnFtpeiiP2ZmZmZmY10LGPfbf55idMmADAiSeeCMDGG2/ccXvdziS4zz77dNxOnfp1h/JGl3bccUcAHnnkkTIfU/iJcsqUKQB8/vnnuZYf5LqI9fHdaKONALjkkkta3u+V9957D4BDDz0UyKILK6ywAgCzZs0Csv7iivCrdUXmzZs3sHWRkrpGdZsjOu817eqrrwZg0003BWDnnXduu94g10Wq3+hxxx0HZK0hOi7D5buNDofra4zW/fffD4yM3Me2M8jXKEnVSep+ouv6XXfd1fFzzjjjjJb/P/zwQwBefvnlrj5f6lAXMakxI3pdZazzR/cZ3XdS541+N8U0qY99rKzuvPNOYGQrSLf3idi9t+j2Q47Ym5mZmZnVQE/z2P/hD38AYK211qp0u7GnGPXhf/bZZwutV6cnzfDpXtk79ttvv7bLp3QbXU5F7MP9rkNdhIaH229G31l94D/44INS219uueUAWGmllYCREfvY50udIvZFM7Gob7v62Pd6Do0lS5YAWZmfeuqpALzxxhsdP3cQ6yJG32ncuHFAPCtU1fM4xK6R1113HZBF5VLbqeM1qqwbb7wRgFVWWaXt+7G6O+SQQwB4//33gZHXqCZFiWNiZafr/dy5c4Gs7IqeLyeffDIAF198ccfPrXNd5O3d8emnnwIjI/ap9VJ1Ebv3dvu7yxF7MzMzM7MaKJQVR2JPIZtssgmQZZYII+h5n45STzmx9/V5lrnmmmuAkRmEiuaQLpshQpkrUtGwQVbVDJuKsKeE8zco8qno17x584B0pD5Uh0wVqWtJ2RzReT839XlDQ/mCWk3IyJKav6GqrByxstT/yqbzta99re1ydVBV9hpF6GXllVfu+Hmp8Q2K3IfjG6qe+XmQpMpOkfrw/Mlbx0cffTSQje8pun4THXXUUUDW86Hfy8oRezMzMzOzGsgVsY89nYR9JG+//XYgy76RV9noVLi8RoU3QdnZTkNVRULUd1h9hRU9fu2110bl88dS2e+gMlJWgzASL7E61eth/7xUloOQohFNpO9+0kkn5VpedRRm9ZCw1bBsK+Ignhd5xzeoRSkldk1Tn2CdPxJmewq3EzrggAOAbPzPIJZ5TNmI4mWXXQbABhts0PK65o6J9aVP7UdYthoHdPPNN+davs7y1pVaN/bYYw8g+/0Vil2jNM/Pxx9/3Ha9JpV5Xiqz1Hw/ykMfSh3POg9uu+02ILsmlc1IKY7Ym5mZmZnVQKk+9qIRwnpCLDoyXvQUolzSTzzxBJDNjtrk/nahsCw0A6b6KKq/aNkMK7HPeeedd1r+hpTZRbMKx2Y7raOyUSZFFr/1rW8B6daN8PMmT54MwBFHHFHoc1WHmgFXI/MHOVqW6pcaev311wE48sgj276vulh22T9dIjVWRLM0KtIYK7O844RSGVsGsS6k2xZY3QfUIvXjH/8YyLJ5iGZzXHXVVYEsw5HKMKyLW2+9Fchm4gyjxnVQ9HgU3U9CmmsgVqe6dx944IFtPy/v8Vx07FedqQx0jVKGubAsFO3VmCqN1QrLvEk9GsoKy1bXBl0zdB+4/vrrgWzm2LBsVfYaU3X66acD2W9a0W/mFVdcsZL9F0fszczMzMxqoKuIfUreKMGXv/xlIOt7+etf/7rQ+k0QKwtlJVD0Vzluu93uSy+9BGRPpA8//DAAzzzzTMft1SniOFqUuSjvjM6aKVMzC0qq7K+99logPltkE+pK0d5/+Id/ALLobkgRSl2LlIlC8mbbCXXbd7JOYt9Z15of/vCHLa8/8MADQBb10lguRTLDujz//PMB2G677QD46KOPAPjFL37R8n8dr1VFZ3HXjMupPN1vvvkmkI1zUART925lxrO4vNeKWKR+4cKFQDb/go7j2FwYRfenTudBXqnvvPfeewMj7wN56XdUSGWvVpeqOGJvZmZmZlYDuSL2qX6rGq0dWy+UyoO/aNGiXJ+bVx0jMvLuu+8C8NxzzwHZ07uE+Vclb5nuvvvuhZaXOpZ1Wep/p+w3IfXX++UvfwmMzPYhKtPvfOc7AOyyyy4tr4fLicbAxCL1MojnSaov7ze/+U0gG4ew0047Aem82XLMMccAWY7zqsoo1v+7SVKRwjAC/8ILLwBw1llnASOzrynSr9aY559/HoiPC2qisIw1K7z6yKfsvPPOAGy44YZANqZLGYl0744d17pfKdIfatL5kPp9E7u2KXr82GOPtby/1VZbAbBgwQIAXnzxRWDkeaQ6Cvcj/JxBug90q6rfmCH1zVe2m7BMDz/8cABuuOGGjtstWheO2JuZmZmZ1cDQ8PAwQ0NDuaaoDJ9KNKI3nI0uVFV0K6T+TvPnzwdGzqSW1/DwcL7pIEdB3rrI67DDDgPKR+zVEqD+2eHTfl55j4E61sWuu+4KZBlVUhTBD7366qvAyJH1opy44eekogEh1dUg1EXeiEbZWXiVXUotX7Nnz25ZLm9GoqIz2IYGqS4klT9e0axQ3muFjnMd97HjvGqDVBepcTe6d4cZ7WLHq8p86dKlufYvtp3YDMxFI5SDUBehvOMfwkwqWm+LLbYAsnuz/PSnPwWyPPWxDEd33HEHkLWeVBWxr2NdhPKOg5Nwzo5wfdVFeO3qttXEEXszMzMzsxroauZZ5b+O6VWkXpSHWDPeFs0EMEiKjlzX8srX/eyzzwKw2mqrFfpc5YjWbKZhxDJlkMu8KiuttBIAr7zyCjByVseQ+t3FxCIsiqKFI+yb0G+12whHbD3VnWbXDj9vhx12ANLZQJp0HqS+q1pBdLyWzeH8l3/5l0CWJarO1/+iUuNuysobqU+tHztfm1RHMWeffTaQ9ZlXJiJdY/7Tf/pPwMiI/fTp04H8ZRhbznWQyVsWOp6VVWrTTTcdlc+NccTezMzMzKwGCuWxD/vrLVmyBMiiuXqivOiii4AsQ8vyyy/fdnvdRtk0w5qekvQ3bEkYxCfQbqOs4Xd+6KGHALj33ns7LhejuQbUF1Mzy3Y7w22dpOpsxowZQNbqEcuhnvdzlO1GWXQ0p0Gq73wd8xanvsOPfvQjAI499ti276ufdozKNuwbrPzfYR/+WB/iOiva/1R5umOtIXmzoml24HvuuQfIzgsbWXa6RyobTiyjXdWfq3uyxleklq/DNakofWfNuyBXXnklkM0towh+2H9bUueLsurE+tg3Sd5rVt4yGjduHADnnXdex+2JrlVVz2/iiL2ZmZmZWQ0UyopTlJ4MlQu6Knr614xsctpppwHZzGwx4VPQII3m7nYUt/J6/83f/A0AX/rSl3Jt58MPPwSyVpjJkycDWSQztn8eWT+SIvbKM1x0e8pffNVVVwEjW0+KRk7rlIlFih53ilzGor1h9pC8da6WAs1kW9Yg1UXe+UvUwrv99tsD2azZse0UzeR16qmn5lq+CdcoUZmEEcWqorap8XhqXUnVcZ2z4qSEfezDe6/OE43/CaXOkzB3elV56+tYF6l7ZdlMkZoleObMmUB85lnnsTczMzMza7COEfu8TwvhcsrbrT7wRTOxlKX9uOyyy4B03vB+ztdd9EkxJrae+tqnZiONUWvMvvvuW2o/YvqxLoqK1Y0i6nnPi1QdP/LII0A2piWvOtVFv/TNVeuL+tzHKELzwx/+sND2B6EuUsK6Ul9iRSZFrSaKYin6VbQ/uLJQaWxLUYPckiXhvXn//fcHYJtttgGyGWNFGbvyzleSd34IRezD+4Uj9iNpZvFLLrmk5fVYmWis40EHHQSk6yRWF6nPSaljXRT9naWWMGXF0ZgW0fmlVnuNl6j6vuWIvZmZmZlZDXTMilO0r+7vfvc7IHsqGe3omT4v7PfdZLEIyOeffw5kT+3KJX3NNdcA8f57opk4LU0tSMpMFEbqy+bfVlRNcxWEeY1DTcxnX7TVMSa1Pc36qEwvmv8htMIKKxT6vEEU+27hNWXddddt+V+zW4etiGrh0oyaor7Csew5ug/p/EtF7ge5zPNSf+3HH38cyGZrV7/tMFIfO5/CmWpjpk2bBsA666zT9v1+aXEbC7FrycsvvwxkkfUw6vvEE08AcMEFF7Qsp3l9UjTOIdyP1H41QbczhGtW4BhF6PU37+e6j72ZmZmZWQMVymMverpQ9Ol73/sekPX5Vb7V2HoxWi9cbueddwZgv/32K7O7Ayn1hKYnwyOOOALIIjF/+7d/2/J/art62lc/PWVYSUVS/uIv/gLIIpVl63wQxcom9l1vvfVWYGRf4tj6ynqjWU1jlAdf/WXDiH1sf+ocJSs7k2W3ZXD55ZcDsOWWWwLxiH2TKSq85557AiOjvqkWp7lz5wJZXSkC/93vfrfjesrX/c///M8ArL322kB2jWzC/A7hd9N1vujMmhMmTADgjDPOaPs5IY1zePvtt3PucXOFdXT99dcD2XVerer/8T/+RyAb56bfYX/4wx+ALNuNWoa1XbXG1PGePFY0nnT11VfvuFysNTJUVWuJI/ZmZmZmZjVQKGIfPulpRtlnn30WKJ/9Rjk9NVJY+Y1F2RB22223jtvRU87dd9/d8n+oDk+syoQS5iN+4YUXALjiiiuArCxS31nRs1jmifBJUlGEgw8+GIAzzzyz7Xp1iHrFxL6bzgu1pvz+97/vuB1FUtTipZkGx48fD2QRx9j5teGGG7Ysr5lo61j2qeM4FfFQGaksq45iVT1j9CDJ+911jQm/65w5c4CsT3yKjnPdNzSr8NZbb92ynOr629/+NpD12S+634Os2+NK1/ujjz4ayGaujXn44YeB7Jq21157dfX5dVS2dTGk+42O77zjH/Lul42k8+Gkk05q+76uKbpGqRVePSRSrfzd9vV3xN7MzMzMrAZyRezDpwdlnVF/obIRDz3NqM+jXHfddS3/F+2vqswUmoH21VdfbXm/Tk+k4XfRSPmlS5cC8OSTTwLw9NNPA1lUTJS1IDbjZuxzmixVFurzqD6QsfUULVb/O/Ux1vmkWSIVwYxlm9J4i3POOQeA448/Pu9XGThF53MIM0osXry45f8HHngAyKK+Zf3TP/0TMLKvZbi/qiNpQrQ4pGtNGFnUdb7oteadd94Bsll+TzzxRCCdoaIJqsq2obEjmqchtZ7GQ3z66aeFPqfJyl4LdD5p7Ero/PPPB7Lc6lacjl/dk3VfSdXZX/7lXwJw2mmnAdk9/L777gNGZjLS2Jf58+e3/B/uR+pzHbE3MzMzM6uBjhH72FPBeuutB8Att9yS60Ni27n44otb/tdMa4p6le1n9NprrwFZFoQ6CWeOTfUpPv3009uuL8oFXdR7770HxLPhhJ9Xx4hN0e8WLq9c0mE2kFjfy9iMzmGdrrrqqkA2E2GdFI1q3XnnnR3fV5aPMGNRGFmPUQuA+u6HUsdI3ghMHSiyuMYaawBZNifNhioqU2Ve0fU8r2233RaIZwZLqcO1Ku/xFMtTH9Jspnnng9As22FLcB3KtleKzrURLh+2Tuq8WX/99YGsZbjs5zdJrCw0h4D61qfOG2WFCrcXuy+FffPV06LotcwRezMzMzOzGhgaHh5maGhouN2bsacQRbe22267tu/3OjNEavvqo6++9inDw8ND+fas92J1IWHEPpUXtai8dadIv3LmpraXNxowCHWRymOvMSipcQsxisjH1le+4lRmClHGImWvCsXqZhDqQlT2ypW+/fbbAzBr1qxCn1M0apU6X8I6DDOyhNsZ5Looeq6fffbZwMic6CGVmaLGimbpf9HYFvWx1zwQ2h+1MsbqIK9BqIuyYpnv/uZv/gaAL33pSx3X0703FpGses6AOtdFKLzGaRzDvHnz2i6nWYVTZVtVpL4OdRG7nquMFIFXVpyUomWra5paLcteqxyxNzMzMzOrgVIzz6r/afik2K1uI/Wx5WOZNNynrLxrrrkGyHKsi8s0iygqX3fRyP2NN97Y8jdcX5mOFIFvQv/smNi5fMcddxTaTtWReikboR9ERb9LKlIvOg9E59XNN98MZGWpGdA9629+qeNYc3HomlZ0/ZQ6Hf9lpa4FYRkrUh8b46ixixrDJS7r/GItS0cddRSQ9bGv+t6bN1KfOmYcsTczMzMzq4GOEfvYU4siLXlH1IfbKyv1OYpsPvTQQy2vD3JEM7bv3/rWt4AsequsHLE+xWHZ9SpLR7j9JkQJYueBIor6G/YJzis2U6eEry9ZsgTIZnR++eWXS31uP4sdn6nMD6GiGY3ymjlzZiWfO0iK9pseGvpTl9wZM2YAcOmll7asFzuvNttsMyDroy9hpL6OZVyV1PF88sknA9lM5soeZdXLOzeH7gOpnhL6TRDmQLf8wjoIsz1J0d9LVY0tSXHE3szMzMysBnL1sQ+fSpQf9dZbbwWyyEtq/ZhUlhFRS8G+++4LZLOoavbUAw88EBg5u+Qgiz3N6zuqn7VGymsk/Mcffwxkcw7kFXvy1OyO2p+33nor13YcNcvoPFHmCJVROFtpWWE2BPWxjEVu6lg3ilbpuN9tt90q2W5YVueeey6Q5RmWzz77rON2Yte6OtaFpKJZmllZEfvY+iqjvDPKhp+r80KzPcZmdTQ466yzgKwvcSgs2wcffBDIzj/J28po8fNE2f2mTJnScX212k+ePBlwGZeRKjPNJZOX6lRZ2vQ7KkbXJF2jNJ6iaIuxI/ZmZmZmZjXQMY99XsqnrZy3eSniHqNIvJxyyikA3H333YU+J0ZPZ/2cf7XoOAZRfzzNThoKyzZl0aJFLf/H9itvX/6Yfq4Lqfq7p3LihudJWHd6XxmKimbhqUPudNF3Oeyww4DsPOhWWObh+RBTdvbs0CDURUzR80F1t99++xXcs1YLFy4Espk3m3RepKTydSsyGUbsY+tproy8MzZXpc51IbNnzwbi94n77rsPyFqCf/nLX5bZja7VoS7yitVF7DetfrPmvW9I2d8UjtibmZmZmdVAVxH70c4206s+Y4P0pJk3WpxX1TMBxvYzr0Gqi5hUNKyq7XVrEKPE3Y7d6FXe7V7n8+7Huug1zfJY1nPPPQcUz5QkTYjYx+4jqYi9osKKEitqPNrqVBcxarnaeeedgSyX+ttvvw3ASy+9VOZjK1eHupCqruep3wK9ypLjiL2ZmZmZWQ3kyorTbVQ4L4/izi+WZzWvsKyrLnvXZXVzAsS2G1uuiWWfmmW66s/Jq9sxJ3WU93jdYIMN2i7fLddJJnYfUSaWvffeu+X9ww8/HIClS5cC+SP1Luu4VJm4zAZf3t8CVc0F5Yi9mZmZmVkNVJIVJ6aqTCmx7UosSpf3c+rUNywlVTZlo2NV1XEd6qIuLVr9WBdFs8xUlcGoqjr1eZFfv0Yq61gXee8H3c7UXHWd1qEuYopeu3r1eyuvOtdFaLTKtuz554i9mZmZmVkN9DRiPyia9KTZ71wX/cN10T9cF/3DdRFXVR/hvFwX/cN10T8csTczMzMzq4Gh4eFGP9iYmZmZmdWCI/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjXgH/ZmZmZmZjWwLMDQ0NDwWO9IJ9/4xjfavv6rX/2qku0PDw8PVbKhCvR7XfSa6yITHvdVHe95uS76h+uif7gu+ofron+4LjKx36wh3dOr/o3riL2ZmZmZWQ0MDQ8P9+zpRk8hqaeSUOopJbWdok85dXzSzFvWobDsyj5Jlo0217EuulW0Dqo6P1wXcWXPr6JUV02oi9g1o6r7RlXqUBdlrxFV33u7VYe6qIsm1kWv7wOO2JuZmZmZNdiyo/EhRaO3m2++OQDPPvtsy/LXXntt9TtXU3mf9MK6CVtZikbNin6+jSzzGJdp/+hV30gbWXZ5WwddF90rG6lPcd1YncSO55VWWqnl/xVXXLHjdp5++mkAnnvuuY7bLcoRezMzMzOzGuhpH/uiNt54YwBefPHFtu9/5StfAeCdd95p+37eyGeoiX3DJNWaUlVmlrzbqWNdVJ3d5vrrrwdg5513bvs5L730Ulfbl0Gui6LHdSiMyMfWr+p8SO3HINdFXlVlkkit16061EXRa1LVddPkuqhq/NtoafK9OyVWNjfeeCMAq6yySsfl7rjjDgBuuOGGttt1H3szMzMzswbraR/7olGBgw8+uGW5cP2vf/3rwMinm7zbr7OyT3irrbYaAAsWLABg4sSJLe+HrSfh++eeey4AN910U6HPtfzGjx8PZHV12GGHtV1O58+ZZ545OjvWB1LRr+WXXx6AtdZaq+V/Hddz5swB4JlnngHg4YcfzvW53UYmq4qQ1llYRrr26O+MGTNa3lfdqS6tuPC423rrrQHYZZddcq0f1knsvBrrOTrGUrfj30ZLOM7OMmVbC7Wezot77rmn5fWqytoRezMzMzOzGuirPvZXXHEFAMcdd1zb97fYYgsgG0FclSb2DVtmmWUA+PWvfw3A0qVLC60fPrFeffXVABx99NFd7Vcd66Lbp/HUeSGvvPIKABtuuGHbz5c657EPv6v6OO65554t/4fL3XXXXUA681Yqa8gaa6wBwKRJkwCYNm1ax+199tlnHd/XdufNmzdwdVFULAq21VZbAbDddtt1XH+FFVYAYPbs2W3fb3K/7lDqmnDqqacCcOKJJwKw+uqrt10udW27++67AVhnnXUAePLJJ9suV7Zu6lgXefVqfE+T6yKvsAxXXnllIDver7nmGiDLkhMuv2jRIgA22GCDnuyfI/ZmZmZmZjUwKnnsJW80LeyPpKebddddt+VvTBP7hKWewidMmADA888/D8App5wCFI/Ux0yfPh2ARx99FID33nuv7XJNrBtJZVhRxHGbbbYBYNasWW2XD4WR+ib2zy76na+88kogi0SWHSOiz9X4hwMPPBCA3XbbrWX7MRdeeCGQnTdNVjbbjah15NVXX61sn+oudm357ne/C2Tje8rSGBa1TO27775dbW8QpVpHyo63aeJ1fqzEylqR+r322gvIrv8pb731Vst2q/5d5Ii9mZmZmVkNjGrEXtS/W1EyRexjT0Uffvjh6OxYDamP1xlnnAFks/nmnQnwgw8+AGDZZf90qCirSF5NjtBLql/2csstB2SR+k022aTtemFEU2NN1B+2yRGcsGx0jVFEJaS+w1/72tdKbV/UynLkkUcC8Mc//rFl+ZhwttRYn8smnj/dRu57FQWrk1jZKOOWsqSFdaDI+5QpU1peD8s8/F/5uvPuR50VPT7Vk8HGTuyatOuuuwLxOgqXf+CBBwD46le/WvEetnLE3szMzMysBkYlYh8+tWy66aYA/M3f/A2QRdXCJ9gvf/nLAPzVX/1V2+1Ymvo4/uY3vwGyiHvsCTSMJhx11FEA7LjjjgDce++9Lct3G11rklgZqcVK4xRSkZzHHnsMGHlepOqiDtGx1DgF0TVGf2OR+27L5Hvf+x4Am2++ea7t+TzJxPoed1tGqRay1HI2kspKkfpDDz205X3NtBkurzLfe++9gayPfZPLPvXdNbZqyy23bHn9kEMOaflfPR7C10Ox8UNaL3x/7bXXBmDhwoVt12ty3YU0rkdZ1arimWfNzMzMzGxs+9jH+iWp/+vOO+8MONJShspIZbnffvsB6WjYxRdfDMDJJ5/c8rryEL/00ktAlolF9MSqCOlpp53W8n6T6jDvd1XE5J133um4nKy44opAlms99lRf57KVVEQjdY2Jba8o9bEP9+Pll18GYObMmS2vDw93Tq/cpPNktFv76lyWVVH+bZ0/77//PpDln99nn33arrfRRhsB2ZwDEutjX+fjvIt5QwC4/fbbgezaEttOmEUwJnYN1Hrh++effz7g1sV/LywL3bv32GOPXOs/+OCDANx88825lu+21dERezMzMzOzGuhpxD58utDMgcocEaNo79y5c4F6Pc33SqyMwghMjGaOffzxx1teVx2++eabLX9D2r5mB26yvMfrqquuCmSZi1Jef/11AN59991Cn2MjDQ21TpIYy+aRilppDMpJJ50EwBNPPAHAueee23b5yy67DIAZM2bk2s86R81i43tE9wtl9AqFx78yC6VmEfZ5M5LKXtHaNddcE8haZtXfOjw/DjjgACA+t4xm4rzhhhvafl74fxPrJuwjv//++wNjVxZhBj3LKPuZxprkjayH9+7YcjH6nLzHhCP2ZmZmZmY10JOIfewpRFGAsB+eKIfz3/3d3/Vitxol75PgbbfdBsDRRx/dcTn10d99992BkU+OmmvgBz/4AZDlQw6fUA3uv/9+IOuXussuu3Rc3ufFSLHIhVqMlFlC+bhFM70qH3fe7YaeeuopAI499tiW13X8/+M//mPb9dTX8vjjjwfiLWlNjFyGdL+IiV3jlKEotZ7LeGQZqi+96No0e/bsltd1/ii6HJvfJByLJamsVoMs73GlLGixFqmY8PgN771XXHFFy/LKZJe6z8g555zT8jl1kvpOsboLI/Xh9mLj3HTv/u1vf1t8Z9t8Too+1xF7MzMzM7MaGJWsOFtvvTWQPXUo2hXSiGH1lYyp84j6bsXKJoxi7bDDDkCWkSW1HWUoivnoo48AeP755wFYb7312i4X68vcJMosNHHixI7LvfHGG0AWQQnHN5SNPtRB7PhRhGT8+PEty4kiknkzSki4nFqkdG3Lu96tt94KOFLfTnhtULRXmVlUp6lob9hKY3GpsSQ6vsNrlaLAqbpQi63GETX5+FbZqCzVSr7WWmsVWl80Q7ki9spIp3E8otaWF198se32YnVS51YVyXs8pr57+L5mNFc2QY1hFM1AG9LvqOuuu67l/6L764i9mZmZmVkNjErEXpH4bbfdtuX1Tz75BICpU6cC8Otf/7rjdopmrLCRmSGKRkyUmUJRhlRO9vnz5wPw3nvvFfqcOki1JOn/Dz74AMj6W8eceuqpQPmyrPMMtCF9J11LYu+r9SNU9FoSZpu6/PLLgXhrpCiLSEyTWiNTkUL9VRmfffbZLcuF9wPl/T788MPbbrfJrYQxebM+6fjOux21BP/ud7/LtR91rJNY2WpGWbVIxcYhxNZX5rrzzjsv136Embvyfk6dxX5Dxo5DleH111+fa/v33Xdfx+0sXbq05fVwP3SfmDx5MgCLFy9uu1zIfezNzMzMzGqkkoh97CliwoQJQDziqMjloYceCsBNN93UdrupfMeOxIxUtizCslXUOEb9+ySWbaSJYsfl6quv3vJ6WOaa9Tc8H0RZFNR/NTa3QKhO50fsu8RmdlUZ583RnCqrsI+8tq+Znqsq6zpH0/Jet9XKotZDCdebNm1ax+3U6fivStn5G1J0jTviiCOA7LxoUotUTNHZp0V96jWOqNu6K7t8HeWdvTc17iD2e0nzluy22265Pv+WW24Bssh9KsIfcsTezMzMzKwGuorYp54U/8//+T8AnHLKKW3XV79sRexDekoS9clXpD+2P1LnJ8yUVFmknvjGjRsHZJHJWFm+/PLLAOyzzz6F9q+OdZOKgCiTSqyfnmZ9vPjii1tej0V4lDVHUQK1lixZsqTtfkkdzpNet9LFtp+KtsXceOONHd8PW76apA7HY12Ex70ytqTG+Wi2X90vtB3lt1dWnPDeXcdxQKl7q3Kip64JyqQS5kSPfU63rSyxFuImiF3vNY5B86OEy4cuuOACYOR9ouzxrHmGlD//888/b7u9cH8csTczMzMzq4FSEfu8I3N33XXXjsvNmzcPiD+5KqIvymd88MEHd/zcOsob2cjbby61XJhbN9Y6o7+xiH2d6ySl6HdX5D01rkFURzp/1Dc/jPhbPCtOVX2Lle1Akcm8HnvsMQD+6q/+qu1+NVF4bdEMnU2+loy2MOIea1WXWGQznMnWMmGZhpnlwtaNqnKux5Z74okngCw6rBbgOs09E/u9FLryyisBWHvttTtuJyzD1Biv1P6UFW7HEXszMzMzsxooFbHPO1NZTNmnl7vvvhuAlVdeGcjy46e2W3T/BkFVs8KF25k1axYA22yzTa7PGRoaKvW5dRY7zpT7vNvtxOpadaY6VOS+jtHfoudyVS1KivhvtdVWuZZXJFNjVkKxvvV1ulaVteyyrbenohHDJvfdr+qcD2e+jAmzoamsFfHX/tx///1Adu+uYx3F7pmp7xYbx1D2eNcMtI888kjH7epzf/zjH3fcfh3qJqQy22ijjQC45JJLgPiYkV59fqjbsnbE3szMzMysBnrax14RyhdffLGr7Wt7ypKz8cYbd/zcvPtZB6nv+PrrrwNZRpbY+w899BCQzYq3//77d9xubAbPJqu6D2LR41d1pr9vv/02kEVk6jBjc6ps1R/7K1/5CjD6MyBr/5S3OOxzLNtvvz2Q9WttorKtgbHjVzPPfvWrX227/SbJ29qX956ZumZobEnZe3f4OXWsu1jZdPudY3Wj1sL99tuvks+pk7DMdO2QcJ6S0RbWVdGeEY7Ym5mZmZnVQCUzz4buvPNOACZOnNjyN6933nkHgIsuugiAXXbZpeV9zWg7adIkIBu5fPvtt7csV4cIZUzsifN73/tey/8q+1Rk5mtf+1rLejGLFi0C4Mwzzyyz27VW9nhLzSD40ksvAfDWW28B8dnrUppwHkydOrXt68o00asIvjKAKdd0rI5Ul02O1IfKznwZi/DXMTd6t1KReJVN2WvYgw8+CMCXvvQlIMsSlVLHe3QsMt7t2MTY54TbWbBgAQA77bRTx/WUJ9+t7+Xpt2o4m3zo2muvBbL7g/ryp6iu5syZA8DcuXM7Lq/Pd8TezMzMzKwGehKxL/v0rZnP/vzP/xzInmrCp6DVVlsNgG9/+9tAFo2LqXOkRmWh2ew233zzlvfzfvdjjjkm13KKBri/XnmpyI0yUagV5b777mtZTtlCNBNzuL0wKl00u04dhBkf8s6qWPa43nrrrVv+iuryuuuuA0bWpfU+autr1EipMu+2zIq2KtapjvLOLVP0O6euTXo/FqkPP/fdd98FshnMY9uLrV9HeVtVHn74YSCbOyZFEfdUZF9U9gceeCCQzUAbW8557M3MzMzMaqirPPaxJzrleFYU+YQTTsi13v/6X/8LyDJGxJ5qwiwfsaeZOgrLUOMN/vjHP7a8L1VFwZThSHMJWFrZSOSnn34KwKOPPtry+tlnnw3A97//fQD23XdfIDvPQqkodR0jL9OmTQOycT7LLbcckOWRX7JkCZCuk6papJSnPhWpj42vqGMdhcrOPxKWVWrWxyaUpeT9zlouPD7DvPR5P2+77bYDsrFdRTPi1VlVM8eG7+sal+p/Ha6vDEUaL1fnFtxQ0XtzuFzZ9VLHgFrhjz76aGDkb9u8Y2UcsTczMzMzq4FK+tjHosSpPlrhU8bs2bPbbi+k3Ot6msnb96wOEZuwTDWifd68eZV+jsrqxBNPBOBb3/oWkLUQvPnmm233J1y/iWJ9LP/Lf/kvABx66KFANj4ipJzQt9xyCwAHHHAAkLXKKBptmf8/UvGF1lhFrCzLXivOOOOMXPuhSP1vf/tbIDt/1GIQ06SomcSiZ7EIvDSxrPJKXX8POeQQIDs/dE0JM3Fphtgwc0pY9pq5U+dHk6//oyUs4xtvvLHt62FdqTVGc234PMoU/a2YWi7vdnS/ePrppwF4//33C+2P+9ibmZmZmdVQVxH7bp9q8q6vzBJ6qtHTTKgJT56x6JbGHay88spAeqayvGWvVhT5yU9+AsD48eMB+OCDD3Jtp8nCutpmm22ArP9pau6AvGNIFInJm6u9Ti1Z8vnnnwNZZFx960VledBBB7UsJ6myOP/88wE49dRT274f1vXPfvazlu2W7U9eR73OgtOE+0FKrAw0/kZ94hXBF/W/1rge/VVrY0iZ7FLHryL6Tz75ZMvrTRxbEhOLuJfNohMqes+uY13EWjFi70tVZRH+plXLbmxMStH9ccTezMzMzKwGKulj3+vISCwHtCMyGUVS1M9ulVVWKbR+3qjAEUccAYyc5ddGih2ff/u3fwvACy+8AMAFF1xQyedpe88//zwA++yzD5Duq1wHKmtliFBu5lmzZgEjj2tleZo5c2bL8qGyZacWgXD/qspp3QTKcHTXXXcVWs9R30yq9eK4444D4Nhjj821vO4vosh/2DImZbOHNEm3x6vqRK31EtalrnG65qU06fzRvTNskdp0000B2GKLLSr9PLWUqeW3aBaqFEfszczMzMxqoCczz8rFF18MwNVXX91xuVSmiXXWWQco/lRfpyfOWKaVUKwPZEwYgUlFEpdffnkAVlxxRaBZcwiUFUZO1K9OkXVlHnrllVfariexutHrmg1v8eLFFe15/4pdC5QHOByHE1te4xGmT59eyX4p8pLKfhPWnaPMxYWZuxYtWjSGe9Of8rYMaUxW0UwpsZbh2Pp5o8VNUjS/vZZXa2LeuWvUr1t97Jt4rUndQ8M5kvJK/YYNhTOj510+L0fszczMzMxqYGh4eJihoaFKO+GOdX+5ok83w8PDnVPIjKJUXVTVaqHtqF+e/j/wwANzrX/HHXcAcMMNNxTan5RBqouiYnW3xhprADBp0qRS233rrbeAkZkmUlLnySDVRRjNOvjgg1teD4/rXlPriyKgUjZKNkh1kVeqlWKZZZYB0vM2aAbmqvupxgxSXZTNwpQaW5K6r4SUBz+c5bTbbDiDVBcpqbqaPHkyAEcddVTL66kyu/baa4HiY1WKGoS6qDrDUFWqbj1xxN7MzMzMrAZ6mhUnb9+vbjWxr1hK3oiK+mP/4he/ALIIftiXXpSpwv1Z41JRgTBK9fbbbwMwd+7cSvejzhlYYtcSvZ46rsvS8Z/y7rvvAvE5NywTO1+UqSV2DZJUpL6J4xdSEfHUvVh97idOnJjr8+bMmQPAgw8+2PL6M888A2StiZ6pPC2so/322w+Aww47rO3ysTJVS1avI/VNMFpzY1R1rXLE3szMzMysBnrSxz6mX3M5D0LfsLzyRmqKjsRPqapO61QXRfUq61PZOhmkusgbkRzUyOAg1UVeqeOy6uhYVXVfh7qo6vwY65mU61gX4TVMs7+nWk+0nuYWCDPWVf3bIFSHukipusx6xRF7MzMzM7MaGNWIfb9qwpNmKBUlqIoj9pluyzZvq0hVLWGDVBdF+xCHykYqu235qmPrSV55y7Cqa5Ij9plBiTym1KEuUnbddVcATjrppJbXVWcamzV//nwgPW9QuL7Pi/pxxN7MzMzMrAYcscdPmkUUjYw6Yj/6/VCbGLEvaqzn2pAmR+yrUnQcULfqUBf90grift3pMliwYAEAO+20U9v3Tz75ZAAuvvjitttN8XlRP47Ym5mZmZnVgCP2+Emzn7gu+ofron+4LvqH66J/1KEuyrawpiL9oz1uog51UReO2JuZmZmZ1cDQ8HCjH2zMzMzMzGrBEXszMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxrwD3szMzMzsxpYFmBoaGh4rHdkLA0PDw+N9T6I68J10S9cF/3DddE/XBf9w3XRP1wX/cMRezMzMzOzGlh2LD/8G9/4Rq7lfvWrX/V4TwZf3rKUWJkW3U5se+F2XIf55S27WF2l6iK1vFk/qPpa5OM8rtv7R9m6im3PrA5i9/Juz5dweyFH7M3MzMzMamBoeHi47/ojVRV9zqtOfcOqehKUvE+YVUVc6lQXZeWNxMeWz9t6kopkui76RxPqoug1ZrTvE1KHuijaolpVC2x4zem2NaUOdVEXrovi9+5ut++IvZmZmZlZjfVVH3s9fRTth+Q+lJluy2DSpEkArLDCCgDsuuuuAHzwwQcAXHPNNQA899xzAFx77bVdfZ6NVDQyX7TOq27Vse75GhZXNrrrssyUPb7ytgZWPWarzsqOLezVmERfe6oTq7PVV18dgM022wzIfkcdddRRLct/+OGHADzxxBMdt586FhyxNzMzMzOrgb7sYy+jNdK+Dn3D8o6+3nrrrQG45ZZbWl7/sz/7MwDOOussIIvYxyxcuBCA9ddfH4ADDzww1/6k1KEuiirbL6/bfrIx2k4T6yKl20wtPi/iuh1bktLkPvZlx0iNVuY6nxfdj2PLu36v+oHXqS5SYmW4/PLLA/Dkk0+2/P/KK68A6TJ+7733ADj00EO72j9H7M3MzMzMamBU+9gXfSrfYostANhggw1aXl+8eDEADz74YIV7N9jyluWxxx4LwLbbblvqc1SHYV3edNNNpbbXBFX3M73uuuvavj516tSW/6dNm9byf9W5pwdBVRH2ouMZws9tQln3SqwP8H333dfy/5w5c4AsOqZWxXA74j7F8TJYZpllgOy+EZZleH5ceumlAKy88srAyGvPZ599Vmo/6iBvJD01fqHba0hqffe1L0/H/d133w1kfehXWmmltsv3+lrkiL2ZmZmZWQ2MaVacmOOOOw6A7373uwAsWLCg5f233npr1PepX6We6tdbbz0gK8vx48eP4t756R+K941UtEzOOOMMIMtMpBH2qQjM4Ycf3vL5U6ZMAeDzzz9vu191jCqX7Zcai7jHIvll+3v3Ou/xIMl7PN54440ArLLKKi2vv/nmmwDsvffewMgos6UddthhAOy3335A1hKbOh5nzJjR8r+uUaLW9csuu6yS/Rwkea8ZsXu3rv/nnHNOrs/T8q+99hqQ9XCI7Y/lF5aZIvVz584F4Lbbbmu7Xnj+hP8rG84hhxwCwL333gvAkiVL2q6Xal1xxN7MzMzMrAZ6GrEv249IT/VhH8pQEyKOKalsG59++ikwMgqc0sSy7JXUeRD+/+1vfxuIRybD9VJR3yb19646i0febDbKfpDKJlV2PyyLZsVcdNFFAGy66aajsTsDJe9x9sUvfrHl/1tvvbXl70EHHQSMjCSGwoii+uDr7z777NPyfhP6d+eNlI8bNw6AK664omW9efPmFfo85Uy/5557APjoo49yfX6T5c0uqOu9Xg+zAobU2n7AAQd0XC52XsT2L8YRezMzMzOzGujLPvZl1flpP6+wDDSz2fXXX9/yeq+f2l0XmaJl8aMf/QgoHqGJfV7q/zopOjNmKoqWN2JyxBFHAFn/bolli9Lr7mNfvVROdpftyONO1xr1FY7R/CeKLMbkjU43KXqc9xoTzjFT1tDQn9LK77nnngD8+Mc/BuKzmjapLmLyXht0vX///ffbvh9ea4aH/5RW/4033gBgrbXW6rj9VMtBaj8dsTczMzMzq4FRidjnndVOI4rD/uCOtHQvbwTl4osvBrIn0bPPPrvtduTZZ59t+7q47vKPNbnkkkta/k9FxVRHyjixdOnSjssrz/eRRx7Zcbl+VtVso7vuuisAu+22GwA333wzMHLOjBiNA1Lf49///vdtPy82PuILX+gcU/F5Exf2tc97f3FZps+f5ZZbLtdyMbHWQUeD08ffrFmzcm0nlWFI95GNNtqo5fVw/E+qBcHnS5zmA7r88stbXg/LVDPJ3n///QBss802bbc3c+bMtq+XbW13xN7MzMzMrAZGJWKf9wlwxRVXbLte+H8qktkkeSMhqeXUHy9cXjMGpjIUhesVnamzCVKzPIYRltBzzz0HwFNPPQXAJptsAmRR59TnKK+xclUrW8I777yT3Pd+UVUEcN111wVgq622ArJMFP/4j/+Ya/2HHnqoZf2iNDNhqk+zpelYUERy9uzZgK89EL/3akZMteJpzEeshSlG21WO9bLngzShlSVWJ1tuuWXH5UWt5LHtfOc732m7HWUkkrKzaNepTlLfLXxf994TTzwx1/ZffvllYORcAqLfV1outh9FOWJvZmZmZlYDPY3Yp/o+KsLyve99D4AbbrgByCKKYXROTzfOJJEJyygsG5VpLAf01KlTO25f2XT0RLnLLru0XS6VPaRJdVM0mqwZYmPrvfTSSwBce+21APzFX/wFANtvvz1QfnbV008/HRgZfejnuip7XGm9V155BcjKUCZOnNjyN1yvqjJRH/6/+7u/67hcP9dBr5VtjYm1OorLNKO+9JqJPIzUxyKX4cyaRetKM3XuvPPOHZerU10VvYbEWiUXLVoEwJNPPtl2+6I6Wm211YCsRVd98/X/22+/DWT3lyaMgyj7HTUubdtttwVgiy226Lj8Y489BoycLVi/n7beemsgq9OUoseQI/ZmZmZmZjUwqnnsY7N3qc+YnjBjy8fU6em+KmGZ7L777qW2c/fddwPZzGmxiL2zHxSn6PDkyZOB+HGsKK+Wu/feezsuHwqXe/jhhwF49913i+1wHygaodc1JswzX/aaUTRyotket9tuOyCL4Fx99dWlPr/OUtcO9QNXXm5FmYuOM2rC/aJsWcaoVVEtwCEd37HP1/rK5JXqy5za/0Guw7ItrHmFc2f8x//4HwF48cUXATjppJMAePPNN4HsmpSqkyZST4cpU6a0/A3LSvdStaLH5grQvVd/886jUvR4d8TezMzMzKwGRjUrjqif3V133dX2/fDpRP2Q1L8ptv1BfoovKu+MfnlzPEssm41msE0pOkNaE8TKQLPRhWJ1s/HGGwPpfPWffPIJAPPnzwfg448/bnlfUYU77rij43YGSXi8aeyIMm2FM8KG6+Xdvsb5aHvKaBSLdil6duqpp7b8n3c/fP6knXHGGUAWmbTRj77qnh6jsVo77rhjy+vTpk0DsvNVkU71/66jsvdG3YOVkStFc9DExg/J9OnTATj00EOBLPd6E6R+Hx1wwAEd39f6av0oOqtv0dfzcsTezMzMzKwGKo3Y533KWG+99QC45ZZbcm1Po7hjmSSaGCXOG5EpWyZa78477wTy57GP7Z/FdTuWRBGW119/Hcj6scb6ccf61g9ynWl8zgMPPABk14zUfAp5x4YcdNBBACxZsgTIrmHqnxpbX3001XrifqtpRa/n559/PgCff/55y+tNuh/EhMebMtFddNFFwMhZ3kO6VujaErrxxhuB/Pnv1dc+9r9aMXXfkUEew5V39tDf/va3HbejMtHs8GHGldAf//jHtp8XK0O1Rqb2s47Ca0Uq6024nu43YcaiseKIvZmZmZlZDfSkj31VT9e33347AFdccQWQ9VMNDeJTfFndRqG++c1vAlnkJkYRGEV0Ui0CelJtwlN+t8fb/vvvD6Rnr4udRx9++CGQZVzRcspfHJvToE7C8+Dggw8GRkbqY5TlSX0oNatv7BoTuuyyy4CsJStV1rHxFKEmnD8pVR23LsuR1xDNbq1Zq1NjOxSRV79utUCpT31sfpSi+yczZswAYI899gCya1xs+UEWlr2y1sRozgH1nddf9Y3X+J3zzjsPGBl1jtX1Pvvs0/J/nco4lGq5FZVhilo5Yn3xqxL73Rcbn+qIvZmZmZlZDfQkYp+KuKhv2Lx58zqur9m79OQZPrXE/q+zbp+mFVlXv9SQ8hqXjcQUfbIcRKmR9Kn3NdPy5ptv3vFzYsfzhAkTALjqqqtyLS+DXOYS+47KrnH55ZfnWu/WW28FRka7UtcYUYaK/fbbr+3yovNI4x6kDnUx1lTWc+bMAeL9ssPlm1j2+s5bbbUVkP9eec011wDZ+DZlUCn6uUU/b+bMmQC88cYbhT6vn/Xq+FOriu7d48aNa7tcqi6aeF6IyiQ1ViQsI2WWKzq/Smy7RbMdxtZzxN7MzMzMrAYqjdjnfSK85JJL2r4f60ucd1a6Jj5xxspC/a0vvfRSIJsZUNlD8j6Zls3Y0oTWE8n7XdWvOxWpDyma/PLLLwNZztyi+YbrGLHUd9Jxrbz1yvUf1o3K8v777wdg8eLFbZeLfY7Oq9QcAIo4avvWPfV7VUSy6AyedTruR8uCBQuAeKQ+bwajvGW/0korAfDFL36x0H7Wia4dyvKke3goLPu8reyaeVbjhEJ1vE/ExO4PqRmZtVw4ri2vbnuZpNZzxN7MzMzMrAZGNY+9MqxoZH6MMlTE+uA34UmyKEU61N9UkUv1qSyraN+vJosdl7vuuisADz74YKntKlL/7LPPllpf6nTexL7Lssv+6ZI2adIkIJvxUlQHN998c6HPUdYDRepjkRxlSVCdpVqyHFVO08yysdzSQ0NDo7k7tRYef2oJSx2XVR235557LgCvvvpqy+t1iCLn3ffw2qEWXo0j2mabbQA4/fTTW5bLew9WHvxYxL4Juv298s4777T8n6pb1aHq7rDDDmt5P/xfNOZEYyLz5sl3xN7MzMzMrAZ6khUntMsuuwCwxhpr5Fr+qaeeAmDRokUdl6vDU3xeqQwpv//97wH4zW9+A8Dyyy9favtly3L27Nkt/2+wwQYdP2eQlN1nRY2VeSUWWUmVeWp203A7TTovQm+//TYAc+fO7bhc0QxCyqITK+OHH34YGJkTuuoZoZssLMuXXnoJiM9IbiNpfI4yzu2www5A74/DhQsXAnDddde1fV8tzRoLtuqqq7ZdronnzRFHHNHy/xNPPAGMnAdFrfSayyOlaAYWy0/3fvXFVxlOnDix0HY0T8r3v/99YOTswDGO2JuZmZmZ1cDQ8PAwQ0ND+aZFDOTNI68Z1cKobigcaVz088saHh7um06asbqIla360CvSkVI2y03R9cv2e+3HuihbZsovrP7YqRzskydPbvt+GAUeLf1cF1XPXRE73v/+7/8eyCKbMWpdjLVUSdk+9f1YF92KlUX4euwa5/Oi+7oIr1HdCs/PZ555BoAtt9wSgGuvvbaSz9H261QX3VLZn3zyyQDstNNObZdT1HfffffNtd06X6PCa43OA50XKeH1/s/+7M+AbJxoqOxvVdWVI/ZmZmZmZg3SVR/7Ak9ybV8Pn+5jkXrnrS8vLGNFh9Xn8fHHH29ZfrSyH9SZ8mwvt9xyQPz4VR3obyxib5my4xQkFvGPtf6lIvWimWhTfP7EVdX64r7BmVRZ6JoT9tf+i7/4CwBOOOEEIMvOodb3WG718HM32WSTttsvq451mrqWFT2eL7roIgBuuOEGIJsdW1JzcDRJty2/6oWSd/1Ui3NYx8rilorUh9t1xN7MzMzMrAZ6mhVHGVvCGTJjuZyVr1j5usOnl6r71TZJGJmsmp4oX3jhhVz7MYiRl9g+H3zwwUCWleCWW27puJ765Smir3zEltZt3vfYNSWkuky5+uqrgSwbj42+8JoyiNeW0aYy07Ur9Jd/+Zct/2suDrX4piL28tZbb+VaznWWSWXSSlFZxnKjqx+5IvqWiUXGNf6z27EoqftP+Duq7FwDjtibmZmZmdVAV1lxJPYkqf5H6peXWv60004Dsj7HKVU95Q/CaO6yWXHKRuhVth988AEAn3zyCZDlx19hhRWA7AnzD3/4AwDHHXdcrs+P1V0/1kWqX5xGrCvfcNH+3jn2I9dyVevHuuhWqsXoC1/4U6zjrrvu6ri+rL322gC88cYbXX1uSh3rQmLngVq0NCdBOBO5ZmGMzcjcqyjwINVF1S20ut+cffbZbd9Xmev+EGbCq3qujUGqi6rkLTtF7MM+9uExoexSTa6LVIaulVZaCYD9998fKD5PUIx+V+l3lnq3FP0d5T72ZmZmZmY1NCozz6Yce+yxAPz4xz9ueX2Q+2NXLTZSXrOShtGsbmn748ePB2DJkiVAltf7j3/8I5D1BVNri57+Y5mQBlHe46+qSH3ZDC82UtVlpO0cdNBBQHZeeNxPcakyU6Q+HLNiaWVnqS56HMfOK/XFf/XVV0tt1+JS17Ju67AJ95W83zF8//333wfgmGOOqWQ/5s+fD2RjtTTmJe/vqFhdO2JvZmZmZlYDYxKx/+EPfwjAzJkzOy5X5yfGqqjPl8YxrLXWWgB861vfart8KjJz8cUXA9mTqfIcL168GMhmDlTGow8//LBle3rCdN2Vp7JUXVh5eY9DtUypBSzmgAMOAGDp0qWVfr6NpPEOmhcixmU8UtkyiUVvY1IZXB555BEgy60ey4jnOiyvaA71oq/XuW5SfevD777OOusAcOGFFwJwyimntN2OqG++7hfK/Chf/OIXW9bX76yUVJ04Ym9mZmZmVgNjErFXlNeKC58MVZaaQXbPPfcE4Prrrweyvo6hOXPmtPyv0dknn3xyy+eET7Dvvvtuy98miT2VK2tNtzMkX3nllQAcf/zxhT7f0mKRwiOPPBKAKVOmtF0vrLvp06cDI/ML1zmqNdpUlssss0zH5VKtK5bW6+N2zTXXHJPPbZJYWcby2JfdXp2UHQ/35ptvAvCf/tN/AuCdd94Bst9boalTpwJZJD6WwavofqU4Ym9mZmZmVgOjGrFXfvpU3+Em9O0qKtUXTBH31OyLWl4ZJ1KfUzZK3O0Mof2oVzMfn3/++QBccMEFY/L5Tbb55pt3fD+8Fu22224AXHHFFUB8pkJLix3P+n/ixIlANlPzxhtv3LJcaqZyGzuffvopAHfeeWfL666j6hXN8KLfYbrfaDxduL1wPcssWrSo5W9VqsqY54i9mZmZmVkN9DRiv2DBAgB23nlnIJuZU/9bceETW94sBnmjvFU9ndcxqhz7Tq+88gqQPb0fddRRHbdz6aWXtvz//PPPA+nIiY2+MKp84IEHAo7U90JY1jqfNHvprFmzWpYP+9g70lid2LVHY6vUchXz9a9/HUhnvrPR97vf/Q4Yeb+xTNlrR2xsYlXby8sRezMzMzOzGhgaHh5maGhoTKYJrao/UbeGh4eHRuWDcojVRdEnt7LR3rGOcg1CXYRGq6xHe+zJINZFTKzsdt11VwBOOumkjuuJMha99tpruT63qrqqU13klTfLVLfZqIpqYl1UrarWFddF/PjXbPDbbrstkLUkP/300wA899xzubaXt27qWBd577n98ltWHLE3MzMzM6uBUcmK477C1Sk6At4Zhnqv6NO8s9uMnbDMNW/D//yf/7Pj8gcd9P+1d+fBc1V1/v+fHwERkECpYYrNYskMO4I1AqJgCJSACkIcijU1BWFxENlRwuKoYZN9GxVIyMwAAQoHAlFZRiACxSKlYSRsspYEKYMykAwii35+f1iv3/32+fT5nHtvd38+3ee+Hv98qvtz7+3b59yl7/uc8z4HALBs2bIe7p39v8pes3ytG3++lvWfs846q+V1p1HnJku1CvbbNccRezMzMzOzDIxrH/t+kWPfsEHluugfOdRFr1tLPN6heVwX/cN10T/ZoFwX/cMRezMzMzOzDAwNDzf6wcbMzMzMLAuO2JuZmZmZZcA/7M3MzMzMMuAf9mZmZmZmGfAPezMzMzOzDPiHvZmZmZlZBvzD3szMzMwsA/5hb2ZmZmaWAf+wNzMzMzPLgH/Ym5mZmZllwD/szczMzMwy4B/2ZmZmZmYZ8A97MzMzM7MMLA8wNDQ0PN47Mp6Gh4eHxnsfxHXhuugXrov+4broH66L/uG66B+ui/6xfC83/qUvfakr2/nxj3/cle2YmY2l8Broa1lcqqxi9xOXaXV1y7LuPb3T7bqOy+t13TaxLnp13PeKu+KYmZmZmWVgaHh4eNyaLfQUVDYyE+rW09AgNCE1JfI3CHXRFE2si9g1KWaNNdYA4Itf/CIAjz76KACLFi3q6n41oS6qlv14RRhzqItetX6MdeQ9h7rotqrR5Sb9jupU2WvUeLdAOWJvZmZmZpaBnkbsq0ZU/KRZPmJfVrejX3W3X9Yg1EVZnY4xGa/+rpJTXXQqLOtDDz0UgC9/+cst7x9yyCEAzJkzp6uf3+S66Jdrk7gu6l+b6t6PYuvlWBfj9TuobJ3F6qZJdREr8/GO1Isj9mZmZmZmGRiXiP3EiRMBuO+++9r+/8orrwRgwYIFACxcuBBwBKaKsep/2u2+mjnWRWi8skVVrasm1EVKrMzmzZsHwHLLLdfyviP2nQvLvO79IqbTa16OdRFG4jfccMO2y7322msALF26dNTtyNZbbw3A5MmTATj88MMBuOyyywCYPXs2AO+8886o+zfI16hej2eo2goS9pBoUs+HqlJ1F54nuhbJtddeCxTnTWw73eaIvZmZmZlZBnqaxz7WN2vttdcG4OKLL2673pQpUwAYHv7bQ5ciMN1+wuxnnUbcy45b6PQpvu74iCbpdtn0etyExctO16YwUh9bv1fRsZyFZZa6X/zpT38C4Ne//jUAW2yxBQCPP/542+VdF4XwOH///feBkce3yuqxxx4D4PTTTx91u6qDo446CoBf/OIXQFGHf/zjHwH4r//6r1L7Nchiv4NSx2HZMig7/4Oiy2olkdtvv73tdv/yl79U+pwm2WCDDYCR1yRdi1RW9957LxCP2PeKI/ZmZmZmZhnoacS+Lj0JrrTSSi2vm6hqxG/GjBlAOqJ4xhlnjPp5VTW5jspKldFpp51WajtbbbUVEK/Dsp+XU1RsrBx33HHAyLJVROall14CYJVVVgFcxmNJmYo22WQTAI499ljA16ZuCKPMYcRe76+//voAnHzyyQBMmDCh7fYOOuggAO68806g6IssOdVZ3T72sch72Qh/+HqPPfYAYOedd27ZjsYLhZTx669//Wul/c7RoPVMcMTezMzMzCwDYxKxr/qUo+Xvvvvutus38YmxrLPOOmvU/6vs5s+fP+pysTpbvHgxACeeeCIAb731VtvtN1nZ432zzTYD4Jxzzim1fhg1mDlz5qjbHxpqTVIQ267PrziVoSKPZ599NlD0IZZrrrkGKPpUWveVbbU87LDDgHiriY/v+vm2tZ7Gv02bNg0o+mNfeumlo66v5S6//HJgZKTeRlLrRt3MQVWXUx1///vfb1m+agtCDmLfTdlvwtaP2HpjXUaO2JuZmZmZZWAg+tg3UewJb5111gGKyGHd7YTKRpn1+XpiPfDAAyt9ThOFZbPddtsBcPzxx4+6fKf9+RRFVlTNfe7j2WpiFKlXna277rptl7vqqqsA9+vuhl7N81A2G4mNlKoT3RduueUWAFZYYYW2yz355JMAnHLKKUDR7zvVgpwjlelqq60GFNeY1Vdfve3yGlv19ttvA51n04k577zzAHjooYdG3b7Pn7TUOLheccTezMzMzCwDfRmxV9Tr+eefB+KjvZvwxBh+93//938HyvftKrvdqtR/Nba9JtRNTKovolqiYi1Sy5YtA0b2kY/RWBTlWJcw8uPc6unzJTyOr7vuOgB22203YGS2qe9973sAPPDAAy3rN7Fsu6Vui5WWV1acSZMmtd2uxWl8wtSpU1vej9WJWgVvuukmIB6plzA3uvrq1+3zPwhS1xhd53W9jl235aKLLhr1c2LXd7WWXHLJJW3X1/KPPPIIAO+9917b5Zqo099Lnc5LVJUj9mZmZmZmGejLiL1yQUuTIozhd9TfNdZYAyhm8FM/u7JmzZrVsj31kT/88MNbXsshhxzSsnwT+l/3StXjteyMf3r/85//PFDMGhlS7vUw0jPeI/fHU+p4Vj7uI444AhjZQiXf/va3gfisqBbXrVa+stmeOv2cHHXr+r7PPvu0bE9uu+02AK6++mqguLY18X7Sre+sa1PZ7eu3g+Z5KLte7Njw+VPoViaibnPE3szMzMwsAz2N2HfrCbXJfexFOc+VP/vxxx8HYPbs2W2XDyPwW265JVBETKZPn97y/zvuuAOAb33rWwDMmTMHgIkTJwJFWS9YsACAjTbaqOY3aS4dt4rubrjhhqMun8pmo/fVLzZ8X9T3XjmmwxaBJopFo3R+vfDCC0CRQ1q0/JIlS4AiH3ds+1Zdr6O5Tbx/jBWVrVoPn3nmGQB23333luVyLvtYhDu85qy99tpAMeO4MgulWlLDsoxRhH6bbbYBRs69EXrwwQcBeOWVV9p+jyYL6+7nP/85AEcddRRQ9KQIqW4fe+yxSp/TKUfszczMzMwy0Jd97G2k+++/v+X1McccA8QzB8VGzocUNdaI+aeeegooIvWhX/3qVwBccMEFLe8748pIsTK47777gHjEXrMxKlr8xhtvtN2OshYsv/zybf9vcbFolDKpbL/99qMup7JeuHBhqc9xf9W0sGx0fmiuDOu+sMw1H8Ptt98+6nKimcjDWVHnzZsHFC28qc9twn0j/M66xypSHy4XloGuNWVbXLWdMJNXbH9+8IMfAMX9JrXdHOsoJiwr1YXKIByTKDr+lTVtrDhib2ZmZmaWgYGI2DfpyTD2XZVHW5Tlpmo/OEXBNMvdqaeeWmo99el/4okn2v6/yRlWQqk6ufnmmwH4+Mc/DhRzEogi9UceeSQAe+65Z8v/lfs59rlh2d9zzz1AOtuONLHu9J3/8Ic/AMV5EYu0q9+qPPfcc0BxnsTGBVl5Gr+gMSmpsSYq40WLFo26nMXF8tjHzJgxA0hHeSVVBznVUeyeWDdn/7333gvE+3OLxlQpUt+rsY5NkPrOyiqoiH3om9/8JlDM4jtW49scsTczMzMzy0BfRuzXW289oOg/3mRVn5JjUQGNwP/lL38JxCP1sc976623gJGRyvBzU9uJLZ+jTr/j0qVLgaLPvf6mhCPwNd6iiRGXUKwMNthgA6B8C9bmm28OFHNu3HXXXUD188BGUhl+4APV4k5qjVRGo1AT+wbHxO4T6mOvaG+Y0UW0XtlrUhOlxtfUlTp+58+fX2u7zpYWFytzXaNSdRJrZe8VR+zNzMzMzDLQlxF79Ve9++67Adhll10AR1zKUBmtuuqqQBF5SeXKjUUTbrjhBgB+8pOfALD66qu3/D82k6DrqBAr23fffbfldaxOwlzqIUX2Y9lDmhw1Tn33lVZaCYBrrrkGKLLhxMSOa9WRzpczzjgDgNNPP73S/jSZrxn9o2xf+DASGd4vlC0nNVN6TnVftux23HHHUsur7MIyjP0e0rVH9/yUPfbYo9RyOdVRVbHr9k9/+tNS62vm8mXLlnVtn0bjiL2ZmZmZWQb6KmIfeypKvZ/jk2TVvPBhGc2dOxco+kA+8sgjQHwGtNh2h4aGgCKiGRM+9buPfboVQ6/VuqI6KxvVVQ7pE088saP9bILY8absUJptMdWSVbZuFC3TeWeFuq173WoVzPm+MVZidaH7xa677grAVlttBRTZc6pub5ClrhVqPU9lJ/vOd74DjMxIFyujVKRe64W/BXIo836julWruuaH+P73vw/0rswdsTczMzMzy0BfRezLjh5vQj/VVF74MOoU62eniEm4Xmy7IfUNiy2nfn/bbrstUEQLFEV+5pln2q6XY4RGqn4X9bvbd999Abjxxhtb/h+W1dNPPw0U+b2VsahX+5cjlanKTn3tY9eW8847DygiL3LSSScBMGHChLbruaxHqpolJLW83lcd6rwI129SXZRtMQ3L9rjjjgPgsssuA+Czn/0sUOTjDtcPPy98/9FHHwWKzFzafridVMvYINVdKgKvTCpqqU0d31//+tcBeO2117q6n+F+We998IMfBHo/E7kj9mZmZmZmGeiriH1M2X7mTdJpGXSrDBUlU8uAtqv3m6xs1Ozggw8GioxDsdaYM888E4CHH3640ud2O49yDlQmO+20U9v3X3zxRQCOPvrotusrj/3HPvYxAI455piW/6vFzApVW2LVH1XqzhHQxPtG2bFY4Wsd9/LAAw8AxcyZoSuvvHLU/Zg4cSIAkyZNAkZG7ps4+7XGIUjqONYcNPqb4t9L3ZO65oR1mVp/rH4XOWJvZmZmZpaBnkbsY5FCRbNSM6TpaSgWZfCTaFxqZLzK8IorrgCKPsQxmlugbJk/++yzpZbPMYqc+k533HEHACeccAIAkydPblkvVmbqr1r2c0JNOl86Pa7efPPNtu9vvPHGABx66KFAMduppZU9/nQ+rLnmmqW2p7pORc+aJNZql2q9i9VRbFbS6dOnA8VYLM36u8UWW7RdXpH71OfJIN/rY2Wtsvze977X9v9166jucjGDXPbdFquLsMUqVWbhNSrVIlC37B2xNzMzMzPLwLj0sY9Fk6tmSbC4+++/H4C11lqr7f8PO+wwAF599VUAnn/+eSBeB3PmzAGKfPaKvOy5555tl9dMnD/60Y+AeMSniXW5ww47AEVksizVza233trtXWqMMKLyyU9+ctTlv/KVrwBFPvpjjz0WGNnfVdvTeXTddddV2o8mC685W2+9NVC0aKXK6PDDDwdgo402avt/l3Ehdn3X9TrVNz/Wf1vzpfziF78A4KijjgKKugwdeeSRo25PBrnuUr9nNH4h9h077SsfW6/sNcrS1JPhn/7pn4DO5+RwVhwzMzMzM/v/DQ0PDzM0NDTcyw+JPZ3E+thr+bvvvhsYmZc43E6nhoeH+6ZzZqd1UbffddXWEs3Yeeqpp466/JIlS4CiL2bKINdFqgyV6/+3v/0tULR2aD21aiy33HIt64XHuTKwqD9r+Pk+L+IRRpk3bx5QlHVsuRtuuAGA/fbbr2W5kKJgs2bNAmDRokWl9qdsXQ1yXcSEZa1+2WoVUfQ3Za+99gLirYLdNgh1kTq+Zs+e3fL6kEMOAWD55f/WiB8ry7LHrbJNHX/88S3rhTodFzEIdZGirGihq6++etT1VEdqfY+tF9bVzTffDJTPi9/ka5Sk7u3KCqh7fGy91G/akPvYm5mZmZk12Jj0sQ+jVJ32sbdCLOd5aqR9rC9jrA7Up/ipp54CiuwgqfUUGWpin+Lwuyqn89SpU1veV7RM4xhE4xmU397Kq3p8x9ZTpD7mlVdeAeCHP/whUMwKnJoltcliZaBWvTXWWKPS+srspVYYadK1pipdc0LrrbceULRA6V6tLFHKcx9ezzUe4oILLgCKuojNyFzWIN43qrZ+K3IeXqPUfzvMJKT3NReAWh019iqM2If7s/vuuwNFS5eVV3UujbBOwzkJdP/oNkfszczMzMwyMCYR+1ge++Hhct2gBulpfbxUndUxJizrfffdFygiN+uss07b5WLef//9Ufcjx7qNRYevuuoqYGS/7tDXvvY1oMhUEW439bk2UlgXb731FpCOKIZ1uWzZMqDo36pr2eLFi0ddL3zfRlJfYfXL7pTLvrrUeDa1HioLjpx//vktf1P22GMPID0WZhDVnfU3fK2IfMqqq64KlP89JT4fuqfs3ExqbfnNb34DwL333tuT/XHE3szMzMwsA33Vxz5cXtk/bKS6s9JpvbLRLM2sVjWyqZlsH3rooVGXy0m3ZgS8/PLL276vMvN5UZ/KXrnPly5dChR5tVPrHXDAAT3cu7x1es73OuOXxe26664A7L333i2vw1bFFEU2pQlR46rjeqqub72Xmmug7vq94oi9mZmZmVkGxqWPfUwTnt67JTY6u+7T/f777w/A3LlzgSKycttttwHpzCzqe6y8xPvssw8A77333qjrDWLWg06F33nKlCkAbLbZZi3vh3V5zz33ACPz11t5KlP1sf/4xz8OFDPLdluTjuteSbVOqvXlr3/966jLNVGsZVbXafXLTpXRCius0LJc1Ui9qLU+lb8+pzorG5EvGxWu2gJQNUNXTmU/VnRc98vvGUfszczMzMwyMCYRewlzrV9yySVAkS1klVVWGcvdaSTNdhrmMQ5nO9VMaqm++MprfPTRR7e8//bbb7ddPjTeT7a9FEbezz77bGDkzLNqDUm1irz00kst24tFZnIu07pSmSrK5hOumrfYdZEWlpHG84R56WPLdzp7aZOEx6XK7tJLLwVg/fXXL7V+Suza5Loaqd+uEb52VaceDp3+pu1WJi9H7M3MzMzMMjCmWXFC6pd94oknArDiiiu2/D/sS+y8xGlhmWgWR/XffuaZZ4CREfqyVAeaDU+z5pXtxxe22jRB+F3VvzuUioapL7FmDnS2hPLGKl+26yQtde5vueWWQDGmRHT8hzSngBWqHue6T6y88sqjLqc6idVFjPLWW32putTcArGWXx0Tun9U7btvhVirxuc//3mgaHUMf9PKF7/4RaB3vVQcsTczMzMzy8DQ8PAwQ0ND1aYs67JOnxQ7jf4ODw/3Tce/unURe4I8+OCDAZg6deqo65U1Z84cAG6++ea2/3ddjBSWseriiSeeAGDy5Mkty8XKcK+99gKKiEtMt1pDcqyLbus0R3VZOdRFqsW1W9nTYq0zTayLbkdh+62ldZDqolOxulxppZUAePnll1tex+i+s2TJki7uXbPqIqZfzjdH7M3MzMzMMtDTPvbditCEPKNgeY8++uio/w8j7xrNHbr66quBIq+9uC7iYmWiMldWHEXg1Vc41hrivsT9p98imIMgla+7bH7t1DXH16RCrPVirMvIudPrcxn1j26fR92uW0fszczMzMwyMKZ97DuN0Me2537d1fun9mvkJIe6KKtX0bMm9iXOXQ510a2sZt2+j1SVQ12UNVYR/bp106S6iOmXft2ui/7hiL2ZmZmZWQb6IivOeGvik6Yj9mm9qoteRVianP0jd66L/uG66B+ui/7huugfjtibmZmZmWVgaHi40Q82ZmZmZmZZcMTezMzMzCwD/mFvZmZmZpYB/7A3MzMzM8uAf9ibmZmZmWXAP+zNzMzMzDLgH/ZmZmZmZhnwD3szMzMzswz4h72ZmZmZWQb8w97MzMzMLAP+YW9mZmZmlgH/sDczMzMzy4B/2JuZmZmZZWB5gKGhoeHx3pHxNDw8PDTe+yCuC9dFv3Bd9A/XRf9wXfQP10X/aFJdfOlLX2p5/eMf/7iXH1eZI/ZmZmZmZhlYfrx3wMZG+IRZVb89kZrZYOr0WhTT6TWq36Nw/aRXdViW68bGUux413FY9nwYq+PWEXszMzMzswwMDQ8Pu29YA/uGpZ4c60ZkOn0ibVJd9DvXRf/IuS5S0bCy64fL9yqKlnNdVFX2ftKrz3Nd9I+c6yK8luy5554AHHLIIQC8+OKLABx33HEty41Xy5Ij9mZmZmZmGehpxH6s+3XX7SOZ45NmpxF3R+zHPgKTKvO6ZVt3u02ui34zCHXRreht1Qh8t6JiZe8fg1AXnepWH/qqrS5VtzsIddGU8W2DUBdVhXU3depUAA4++GCgqJvHHnsMgNNPP73temNdh47Ym5mZmZllYEz62NftA1k2euwo8Ujd7l86VhH8JtVFrGzGOloWk2NdDKpBqItuHedafsMNN6yze1x55ZVt33/nnXcAmD17dsvrGEfsC1XHZHXa2h7KsS7GO7NQyPeLkcI6uvjiiwF46qmnWt4PI/bjzRF7MzMzM7MM9DSPfadPpGXX73XfyyZYbrnlRv3/7bff3vJaUbE333wTgHvuuaftemOdNaEfVf3udbN7hFz29bnsOlc2Ur/tttsC8O1vfxuAL3/5ywAceuihlT6v7Hkyd+5cYGTEvsl1nSq71PkQez+8r+i+cfjhh7e8H95fQjNnzgTifZgHQac9FLo1xip2f/HvqHw4Ym9mZmZmloGeRuxTT3rh/0844QQAVlpppVHXe/vttwF45plnOti7vJV9yl555ZUBuPHGG2t9zv/+7/+2fJ6ibX/5y19alhvvUeJjKTWmJIzMhK8V5dp0001bXpelsn/yyScr73vuOu07HBOLtuV8nJcVK8ONNtoIgNdffx2AefPmjdUuWUWx4/i0005r+/5JJ50EwIQJE1rW17Gg/N9lz4+bb7651P70s9h1P7WcdKuVwteokWL34kHliL2ZmZmZWQa6khWnWxlYzjrrLAC22GKLUtsdGhoadbkm57GPUVnttddeAEyfPr3l/yqzZcuWASMj7yuuuCIwslXljTfeAGDatGktn5MS1tEg10Xd7E8rrLACAKussgoAq622GgCXX355y3Jlo8vXXnstUNRFXYNQF+Od/alu9o+q/WkHoS5i9B11vVYUV8dp3exQVcs+do1KtayFBrkupNPzZv78+ZW2U3a7MbvvvjtQ3LdyyGMfK4ODDjqoezs1Ct3bda+XuhH8QaiLmLLX5bfeeguA448/vuX9snnsx6q1xBF7MzMzM7MMdKWPfaejuS+77DIA1l133Uqf675ivXPAAQe0vFYUYeHChQCcc845bdfTcoqONUnd7Dc//OEPATjiiCN6+vk5Zj0oG+1NjW+IbbdstpDY/qT+n+pPO4h1EyuzMFJ/6623AvExJL2OcnUr+9Qgqnqchno9t0z4OakxW4Og7O+g1VdfHRgZudd5I51G9r/xjW8AcN5553W0nZz0KpNj6nre7fPJEXszMzMzswx01Me+0/6o6lOfyv4RPu2kZvmqGu0a5L5hZYV1FetjLypblXVYlpoV8rnnnmu7fmr8gwxCX+JuR4cUKfn5z38OxDNL1I16VR3vkGNf4rp9iC+88EKg6EsZCutK2aTCaFq3ZxWeP3/+wNRFqmXommuuAYrIZNn1qwrrIDwv6hrE86Juq0d4Lz3//POBIqNRr+hzly5dChTn3Ysvvtiy3CCdFxKWadnfQVWl6lzZBR999FGgiNw36X5R9lpz0UUXAXD//fcDMHHixJb/dzrzbLdb0x2xNzMzMzPLQE/62Iev99xzT6B4ylGkRtlvQp32qRzEfqm9EnsSVES9rlNOOWXU/yvCEov4D5LU8V12/c033xyAHXbYAShm7S3rzDPPBIrzRudVbD/uvPNOoBnjHapGeV966SUADj744Jb3jzvuOKD88brvvvsC8KEPfQiAWbNmtd2vqjN2husPkk4zBYXn1/LLt96mNHtpmAu9W/1jB/la1SmVgWb9DefWkFQZ6TwoW5aqU7n66quBokVYkfpc8oz/v2K/g3pNme223HLLlvebPPYkRq0qU6dObXn/4YcfBuCjH/0oUL21o+oYrth2Qo7Ym5mZmZlloFLEvuqIXz3dPPHEEy2vu7X91HaaHHkpq252BEXLYs4444zOdqyPdJqxZOONNwaKKFgsM0q4/Y985CMA/PnPfwZg2223BYrsUZo1eMmSJQCsscYapfYnJ2WP18cffxwoolQaI1K2L3wYTQ6XU2tMKFXXIc2+qjEwOYpl81BZnnjiiQDssccebZe75ZZbAPjd734HjBz3kMoPnmrJyvH+UfW76HiuG6lXFDjMZhOjOi1rEOsmvKaotVDX+e233x5IX5M6bUGOSWW0G8QyT+m0xVTXILWmV13/4osvbnmtcQ/f/e53W17Hthvbf0fszczMzMwy0NU+9iFlPwhn6Uptx33sO5d6UlQ2HPUR1tN6quzCPsmhqk/AOdfVOuusA8Cxxx4LwNprr912ueeffx4o8nqvt956AHzmM58ZdfuKOj/44INt/685B9RiJjn2mYwdR5ttthkQj6h3Whb63E022aTSdjU+QnW44447AnD44Ye33X4O9tlnHwAWLVoEjJy9Wi655JJRt3PkkUcCxWyk3ZZTmafoONX1X+PgdFym1lu8eDEA77zzDlBkdtG1p1tyqJPw981rr70GFNf57bbbDhh5Xlx11VUtr7W+7hu69uh1aO7cuQBsvfXWo+6fMns16R6dovNCv5NC4fmTojFcut6H44dEvxnUAqZjpSxH7M3MzMzMMtCViH3MpEmTgPgT4HvvvQcUfR41A2dI6+vJ9Z577hn1c3PsI1lXaqZN5eEOnzivv/56oOhjqT71ijSG2wnrsolmz54NFH3flXlolVVWaVlO/b2fffZZoMgjrEhmmCUh7Oca1kXYt151o9mBw77KTTovYjMkh8K+wGVzSasu1QKm8Q6h8Pz7xCc+AcCaa64JFJEbXeMUocmprtRfdMaMGaMut9tuu7W8Xm211YCiDGOR+lhZqc+wcqLrWlV2/SaIRSRjcwGoLDUeIjbvQ2x7qWwfsXFNsf0aBKkxhDGK3up6r9ZHZWpJUd2EEfvwc5uURS0U+30UOy9CU6ZM6cp+hJ9/3333tWz/1Vdfbfl/7LxwxN7MzMzMLAM9idirH3aYm1YUufnVr34FwBVXXAHEI/by0EMPAXDvvfd2ZT+bqGzkIxytHYsqKPuNIjiqy5RBjLhER6B/4G/Px2Fe7Zi9994bgOuuuw4o+t4rE4rKVH0jV1111VL7pTpS9PnJJ58cdb0mSEXDwpljFR1W1Dg2O6qoD+Q222zT8r76yYYzdE6ePLnt+zGDOB6ibEah2PIS1sV+++1XaT8UebzjjjuAoq5Dg3gtqqvujMzy+9//HijqRhmJnnnmGWBkFo+68zd0e+bmfpBqPS+7foq2v/766wPxVsSbbroJKMa+pDIY5dgTItUyNNbCMtbvMLXaaBxFaj8dsTczMzMzy0CliH3ZJzXNGhejJ8Sf//znQJE9JyXsf1p1v3JWtv9e1af+GEXoH3nkkVLrDXIdpaJHYc7xVJkrr/3MmTPbLjd//vxRt5faz2XLlgHp2YFzjMCEYrnQQyuuuCJQlEkYHY6VkSLzYWRfebxPOumkUtuRsn2VB0nZ41dlqbr44he/CMCHP/zhWp+rsVhqGUvJ+XxI1YFaCbfaaqtS6w8PD7e81ngijdVS5L7b94NBrpuq0eGy4xFi27vooosAuP3229uur1aW1DVyvKPYYyks09R5IWrt0L03NG3atJbXYZlqXJ3GTej80nmlyP0xxxzTdj/D7Tlib2ZmZmaWgVEj9lUjGOFTfIwyVShvq/rrdWsGtSaqO4Os+nGXLXPlLVY2hLJcp70vA21f/cXH+vPHU9XoV3g+aM4BtSaWnY8h/FyVfdl5ISQ8rwbhWlg1s0nKyy+/DMDXvvY1oIhW1d0fZc/JMcNQr1XN4KL7yP777w8UUeLvf//7pbaTc9106xyuOu7gsMMOA2Dq1Klt/6/xEWVbNXPW6fGn8WypVvLY5ykLm6556sWSGuMV254j9mZmZmZmGRg1Yt+t/tihFVZYAYg/jXQarcr56b+sWBlMmDABgDfffLPUdtRPVZmIFP1SX+AmlXXseFTZqG9wbBa6qmVV9/gP+/N1uh/9LBXFUkahWJ9gjXdQ1oFwzoHYdmP7oWta2TL+3ve+B8ADDzzQsv1B7O8d7mvZfd9pp52AYtbdslGqGI2PCOd3SNXdIJV1XaksNZoZs+q4Hp1nev3KK68A8NxzzwHFnDZN1OlxlWqNV9mr1SS2vn9PdZ9+H2k8Q106P9THPpUJL3Utc8TezMzMzCwDtfLY62lB0a1YvnoJn/jK5nAOnzTPPvvsUfcn9nlNprI59NBDAfjyl79caj09QSpvqhXC41KtH8qX/e677wJFBFJOPvlkoBhbcttttwHxLFLheaU8+eF+yGOPPVZqv3MW5nC+9NJL2y536qmnAjBx4kSgmEsgpdNc1HLmmWcC8PDDD7dsL/ycQRZ+p/B+oVaST3/600BRF51S2Wr7KZ3mds+JIo+a3TS03nrrAemZNnU+aWblQRgz0u9S14hYxD5cT33rdZ5Y9eNS925lagzvB7HXEr5/wQUXACMz4onqSuMhUr/LHLE3MzMzM8tAqYh9LDqlvvLqt91t+lw9nYQ505ukbNaPUNVIvSi7gaLAZbOEpPYnZ4rc33LLLcDImfz01L3hhhsC8adulaW2I//8z/8MFCPoY3mRY+dJjnWSmrU0RvmJpWwkPnUePv744wDMnj277f8Vrd52220B+NjHPjbq9gaRyki5madPnw4UY1B0v4hl6+iUzi/lflZ2ELXefPe73wVGzpIayuF8qRshV53F3H333UDRKqL5Gsq2fFlc3WxSKcq4peiw1aexI5IaXxTeX8JrZGpsl65h4W+CGEfszczMzMwyUCpiX7d/XNWoV7i8njAvueQSAN5///1a+5GjVJ3ccMMNQDpSH6ujMKIZ06S6SD2Vp/rVhTbZZJNSn3vfffcBcO655wLFU374uSmDmGmlW1J1ovd1rbn11lvbLnfhhRe2vFYfS0VUNDNgrG5T2RMGuW7CstU4HUXQY8uXvU9UvQ899dRTABx//PEtrxVl/vOf/wwU/WXDFrQcWiFjkcKqs5qGdt55ZwB++tOfAsWcAan1UhleLC4su89+9rNA0aquuThifvWrX436/xxnjS+r6rXlk5/8JFBkxCs7p8fkyZMB2HrrrYGiFVE9K0TLL126FEi3QIccsTczMzMzy0ClrDjh08d7770HFE8VinzE8hCnnobCaIL6EylSH+6Hxcti3333HXU9jYxXhpaUWHStyXVRdXZGKRsd0HIzZ84E0pH6sN9fk6nFSX3ZJRYx1HiIbbbZBoCf/exnpT4nzH8/a9asejsc7I8M8vmlrFBhPvmYsvcHSWWcCK200koAvP766y3vX3755UARdW6CumUt119/PTCyzKq2Cg7y8d0tVVstdM/+5je/WWp9XdvUmqgoc0yT6iT1XTUnhspMWZ7OP/98AD70oQ8BsMEGG7RdXzMw77333kCRBU2ZIcNIvX5Ti36nVT2vHLE3MzMzM8tArTz2otlHDzzwQKDoB6TZHEN66tAskHrq0Yj6cDlFWHbZZZdOdjNLsYjKjBkzRl1P/Ug1J0CsL3Hsc5rcTzvUq9zMynojOg9Cc+bMAWCHHXYAilkjY3Kus1hdpHL769q144479mS/pOqxMkh1Fftu4Wy/MZ1eUzotK+1nk3Kth99VEURdazRzrGYal5VXXhkoIpmx7Wr9V199tZu7nbVUZi71qVekPkXjeTQ7qjTh+K4qVia6p86bNw8o7s2qG7WexH6PaT3Zc889236OMnQ9+uijANx0000ALFu2rGV7ZTlib2ZmZmaWgUp57FOuu+66UdfTrFlbbbUVUPQdDp9Ub7zxRqDId9zk0dqh1HfedNNN2y4XzkAb5k1V1Eq52BX5V15ui6ua4z9l1VVXBeCaa64BRo5ZCed3UPaRbn1+DvSdTz/99FLLj1W0NqdrVre/S9nrfKqvfaf7lUPmlrJlqdfbb789UIzj+chHPgLAZz7zGaC4JsVmNw0/V5mQXnjhhcr7bq122mknoMjulKJrXtk5aKwQuw+od0pszqaqZfzOO+8ARaT+17/+NVBk7gpV/Q3siL2ZmZmZWQYq5bEPxfpfp6QyVign9Oc///lRt+P+3iMddNBBbd9/8MEHAfjWt74FwJIlS1r+r0j9tGnTgOoz1VpcKntH+P61114LwK677lpq+2XPgxzPl7L56etu1+rTLLud5qNP6fU4oByOhdh3uOyyywBYd911W95XH3m1GmqW+XB7nWZxyvGalJL6zlXLIlw+NgdN2VzrTayLFGX4UgbII488sqPP1W/f8LwrW0cpjtibmZmZmWWgVlacXvc51GjucERxDn0fx4v6UErsqVwR/zfeeGPU5ay8stFkZaZQXcVaX55++mkALr744lKfn3MddhplStVNzmXXLVWvw3Wv277eV5c6fjV+J5aJK5aRK1YXyprzuc99DhjZxz6neRrqKtsDQr+DNIYqFlnX66GhoVqfW/b/OSr7ndXH/uMf/zgAjzzySKn11HqifPTh56bOh7rniyP2ZmZmZmYZqBSx7zRioqeNVL7t8MmzW33RchaL7obCMnvxxRcBOProo7u+T1aO+ttpRkxlpkjNoHnFFVeMwd71t25H6q17FNXS9Vyz9F511VVAenbsToXjiET9ZWX69Okt+5WDXt0bY+eNcqXfcMMNQJH/3udZXKxsYpF60Wvdu1OReqsu1aqiMSgpWj41n0q3f+M6Ym9mZmZmloFKEftO83WrH9+UKVPa/l9PqD/72c9KbbeJEftYnytlUjnggAMA2H333UttT9lwwu03sWx7LVam3/72t4FiTEnqPFJ0zHVVn8usd8KyDSPhJ554IgCnnnpqpe2q/7Yi7sq6E1q4cCEACxYsaPv/8Nqo2VFz7v8dfjf1rS/b0hujbGu6/4Qz1Vp1qXlJNLfMOeecU2m7vl+UN14tTd26Bjlib2ZmZmaWgVpZcarSU0c48j6kmWs1o2a4vvvrpX3hC18AYOONNwZgzTXXHHX5WLSq17M7Wvy8iB3vilTm1BfYBl/Va8HixYtrfc7OO+886utYtpAmi5WBZr5ctGgRMDL7jeaSkfCaoxkzNTNt1bL2/aO+Z599FoAdd9xxnPckX50en+P9O8kRezMzMzOzDAwNDw8zNDQ03I2NpSImyv4xY8aMtusrW45Ge4/V087w8HDfDCuP1UW3Zxft1wjLINRFVak6mTlzJlDkfA6jY4qezZkzpxu7U1qOdTGo+rEuehWVqjqmqtuR+dT36ce6iOlWJrvUdsYrMjlIdRGKtYrrfrDVVlu1/F89GWbNmgUUrS39UjeDXBe5ccTezMzMzCwDPYnYp/Rbf+1BeNIc7/6ifuqvr+p50S9yrItBlUNd9OoaNtbnTQ51kYtBrItUi9SBBx4IFOPkRLP5nnvuuZX2y/fu5nHE3szMzMwsA12N2A+qJj9p9kurieRYF/3SB7KqHOtiULku+ofron/kXBeD1tKbc10MGkfszczMzMwyMDQ83OgHGzMzMzOzLDhib2ZmZmaWAf+wNzMzMzPLgH/Ym5mZmZllwD/szczMzMwy4B/2ZmZmZmYZ8A97MzMzM7MM+Ie9mZmZmVkG/MPezMzMzCwD/mFvZmZmZpYB/7A3MzMzM8uAf9ibmZmZmWXAP+zNzMzMzDKwPMDQ0NDweO/IeBoeHh4a730Q14Xrol+4LvqH66J/uC76h+uif7gu+sfy470DZk3zpS99qeX1j3/84zFd36yflD2ew+VCPg+sSVLnQ4rPl/5Rti7L1pm74piZmZmZZWBoeHi48c0WbkLqH02oi7qRlrGOsORYF51GuerqtO5yrAvpNBKv9cfq/Mi5LgZNE+oiPL47je7G1vc1qnOxukrVXbevcY7Ym5mZmZlloKcR+149GXY7QpPjk+ag9kcdhLrodn+4unodqRyEugh1KyJftkyrfl7duhrEuqir7HE9Xn2Mc6yLXpdlr8YFuS7i6kaJ695X+qku9thjj47qou7xWbXsevU7zRF7MzMzM7MMjGtWnLJPK+PVLzYn4ZNft8u0X1sAeqHTskxFULq1vXC7TRDr2xgT6wPZq2tOk+sq/O4HHXQQABMnTmx5f+HChS2vY2WSc1mNt6oR+NQ1zXXVPSrLDTfcEIArr7yy7XJPPfVUy18t9/zzz/d6F/tG3YxzdVvlq35er84LR+zNzMzMzDLQ1T72ve7z26toVz/1DatbF72OBLovcfcdc8wxAGy55ZYAHH744UARWdFrCd//xje+AcDjjz8+6uc0qS6qHqdbbLEFAOeeey4Qj36FdREK13vzzTcBuOeeeyrtT6yuBqEuYv2oY3Uyf/78luXC7Sy/fGuD8r/9278B8NWvfrXqLnfVINRFr43V/SAlx7pI/Y5abrnlAJg3b16t7f7lL38BYK+99hp1+Rz72NedIyOmauu97jeTJk0CRt5XVDedcsTezMzMzCwD49LHvtsj8N0Hv/qTaK+yILguCmFZrL/++gCsttpqABx44IEAPPfccwC8//77QFG2YURG2zvkkEOAoq/kscce2+U9HxydZihaZZVVgHhZi8o8lZ9Yr7X8a6+9BsCTTz4JdC8ik4M//elPALzxxhsArL766i3/1/kQWrp0acvfM844ozc7mLFUlppU1Fjvn3baaW3//8orrwDF8d+rDHlNNDz8t2D022+/DcAzzzzTdrn11lsPGHlepTSxTvSdZ8yYAfTuej19+nSgGPcQ3nd0LZw2bVpHn+OIvZmZmZlZBsZl5tkwGqDI5dBQ+y5a77zzDlA8ocY0qS9xqGqkfIUVVgCKiGVZros41YGO4wkTJrS8vuaaa4DuRUQuvPBCAO69996ubC80CHVRNT+2MrHouO91C5MiMA8//DAAixcvBopIUNljYZDqomxrnpY7+OCDAZg6dWrb5VLCz9N5oTrudgRyEOoipex4iBVXXBGAu+66C4C5c+eW2v5HPvIRAC6++GLA94teOOmkk4Ai+huWoc6rq6++uuV9XXvCMSyd6qe6CPvYq2x22mknAI4//vi26+n4V8Rc1++YqtmjdD6oznr1+Y7Ym5mZmZlloKNHtrLRslgERpH6a6+9dtTPWbRoEVCMKLaRqs50tuuuuwJwxBFHVPqcT3/600ARgbSRFKlPHdehVL/tulmnejXrYz9JfafLLrsMKPqbVm2pqkvjKbbbbjsg3Xey15nF+kHqu1WdzyFcbpNNNgGKsSfu312f+gT/6Ec/AsqXmfrYv/zyy8DI+4XHYpUXlpVa29X6F1v+qquuavv/ww47rIt715/G+tyO3atDKvtYi0FZqe/niL2ZmZmZWQZGjdj3agbMmTNnAvER9eHy6jNp1alP2ZQpU1ref/fdd4Hq2RCUd3WllVYCete/exDEjvdwrEjZ6MEFF1wAFP3qHnnkkZb/h6//4R/+AUjXQdXWnBwpUq8+9mUza3Vadlpfn3/++ecDcOKJJ3b0ef0oVaarrroqUGT1UDYbZe8Ya02eBThG3/m+++4DYIcddmj5f9kWJd0f9Dfna8tYe++991r+hm688UagyHdvxfGnPu6atbdXn9Pr9VLnoSP2ZmZmZmYZGDViH+svVLUv/Z577gnAJz7xCQC22mqrUbdvnVOZTpw4ESjKvFPaXhhla2I/1tjxq771GscQyyN82223AUXWgvPOOw+Axx57rGX7UndOgtT5qv8Pcl2lIhiK1IfLx4SZJGLUgvW73/0OgDXWWGPU5TfaaCOgiKbtvvvuoy4/iHUSK9swgpi6JlXNOCGKxp1yyikAnHXWWbW23wSxMvjXf/1XID5zsspe44nOPPNMoJirQ//XeAfNtGnl1Y3irrzyym3/r7pW3/s5c+a0/Zwczwt9p7feeguIH9e6d1cdH5eia9EvfvGLUfev03lZxBF7MzMzM7MMlMqKEz5NlH2qUF7iJ554AoDJkyePuv2ymhgdLqtuJomy//esv3FVI4wLFy4E4KMf/SgQjyKE23d2nPKUlaMs5Xi+5ZZbKi1/+eWXA3DUUUcBReQ+dn5ceeWVLZ/Tad32g26NySrbUpwqI83MrJmdn3766VKfX3b7gyj1nXXcamxWzO233w7AsmXLWtare//JsazLip3zqTJRRrvf/OY3Le8rEq/W9Ztvvhkoxk28+uqrLZ+bo25fRzstq+233x6AE044ARjZx7/bdeGIvZmZmZlZBmrlsS/7FKT+pLFIfSj21BLmJbaRqkZKFM1K0ZNlWOfqp7fXXnu1Xa/JERgJ+3WHTj/9dKCYAXD27NlAMbvvV77ylZbXUrXVRPuhCI4cd9xxQNFP/LXXXgNg6dKlo25vkKyzzjqVlg/HG5Q9r9T6ovUOOeSQttsNWz1feumlttsb5Mh9ivqvxsaepMaSlC0TXbt03/j4xz8OjDzPcizjssIy/eIXvwgU16aY73//+y2vdQ3Zd999W97/wQ9+0Hb9Jpd5TOz4jp0Pup6nrlGK1Ouer7FdVfdjkHR73zvNjqYW2k7nECi7H47Ym5mZmZlloFbEXjPG3nDDDW3/f/LJJwPFbIvhU4z6pSpLQtifNYxu6SlnrGaLzInKbptttgHg1ltvBWDWrFml1p83b17La9WJ6rDJwuNar3WcKvKtzBEhHeeaUVk5drUdnV9a7rrrrmtZX5HHGEXRPv/5z7fsV7jf+lxl40lF6/pRLJKR6q/9+OOPA7DpppsCRQtU3f7Xyo4TCrenPsmf+9zngCLin7MwW1RdKstU5F/WXnttAP74xz8CRW51Rey7NcNzTmLnjcYraPZT0ZwEIc3b8PrrrwNF/29LC8teLVAaz/PUU08BsO2227Ysp7Famg9Fkfqc+9SXpfGeIR3vOq4154bGkJQVa2156KGHAFhzzTVb3q+q7NgUR+zNzMzMzDJQK2KvyOH8+fNHXS72tK++vXrauOaaa1peS5h31X3s06qOrA+F0SpF/JXhSO9rpL2NpJn/lDNXr5VfOMxaIOH5st9++wEwffr0lvfVjzXsxy2qo7C1JSbs9x3OcNvPyh7fseXUcvXCCy/U2m5IrR1nn302UNS9dCtPcT9LjU/49a9/DcCnPvUpoPrspJrZWS3HG2+8MVBE5sPzStRCnMpRPchln1K1VSJc/j/+4z+AopV97ty5QBHhdKtHfbHzRsf1L3/5SyB9Xb/oootG3X5KjpH98Hi8++67Adh5553b/l/H9R577FHrc0L6DatrUNW5oKpyxN7MzMzMLAO1IvYxsaeNRYsWATBjxoyW95WtQ39jTzGKGt97772VPtfiUmW2//77A/DBD36w7f/1BKqMLq6DuDBTxB/+8AcAvva1rwEj+6uKzoe6Ud+Ym266CYB99tmno+30I5WNZjUd63zGH/jA32IlYZ1JOAOixl/0OoIzFlLjHEQzLJ900kkAfPrTn25ZLvadFT3T/998802gaGFSK8nmm28OFHWgsSWKKqulOdxeiqPPRX/uVGu9qGw1w7Iyb4VctiOPQx23p512GlCMgQqXH68MMP2s7iztsTLVb1S19sVex4Szbsc+JzXzbdlrlSP2ZmZmZmYZ6GrEPvTggw8CRdaN2JOg+tjXfQLN4QmzW2KjpsOyTeXKVR+z2AycYT7WJtZB2f6pojJed911AZg2bRpQRBIV2Y/VWbdmylSfTX2+pPoeD7LUtaVqZLxqnvuQzp9U1oVBPK9i2aJE30mRe82gqeW+8IUvAMWMzGUzcH33u98FYMUVVwSKDGC6lsV0Wpc50HdXS6xmlpUm988eL8p9/t///d9AcVynzq/h4eG221PL2MMPP9zV/exnnZ7bunboGhRm9NLrVMQ+puxv3qr774i9mZmZmVkGakXs1Tc3zJ4Rvn7llVcAePvtt9tuJ/aU4xH11dWNHqeE/b8V1Y31IW5i3VX9zsqdPnPmTABWW221tuvXzWiUykOv81J5jsP1B7Huqs68rFzPyoYTZsVJrR+LQquPfYr62DdBLGqmrDaf/exnW97/85//DBTXmqrHpZZXdpDYsaEc66qz448/vtT2c1A2QhhbTi1NixcvBorxEiHN5aF+4lX3o8nUx14zkHdKudTD7IShHOokNX+J/mpuAJVJ+N3Vml71c7ut6nYdsTczMzMzy0CtiL0i8Bql3aunFOWYjmXDkUGONPa7sGzPPPNMoIjYu09lIXYc6v1TTjkFGJkFJFxOyvYPvPrqq4EiS0iYPSEmp5zTsdYOtYqEXnrpJaD8TH6pz5Mw81es7pSLPUdly1D551Mzx9Y9TtWCvP766wMwZcqUlv+fcMIJQJHZq4nC41OZhdZaay2gmL1a1O/7vffeA4oocIxmctY93PeLkWLX+bAuRHWiugjF5jcRnQ8xOdwPQrHvoll5J02aBBTjQbu1/fHiiL2ZmZmZWQa6Eqoom4mlKuW/t86Vzaiy3XbbjbpcKhtOEyMyZctgm222abt8uB2tf/LJJ4+6PUUbYpmLUnKMzJSlqFfZjCsp6qup8yd1HijaprrLqfVEqraGhOMXlNVGLbex7cZ87GMfA+Af//Ef2/7/6aefBuCoo44C4JJLLim13ZyF4+TC80PHq2YLViaiUNlrYk7He0zdbGZhXYTLxa77YcTeWZ/S1yLdS4899tiW99X3PqbT4zZWJ3feeSdQjINzH3szMzMzswaqFLGv+3SSmmVLeexjT5ZNftKsK1bmqTrUbKihBQsWAHD//fePun7OkZe6lP3p3HPPBWCLLbZou5xmZVQf/CeeeAKATTbZBICvfvWrQBEdvvDCC4H0GBQb6Xe/+x0Al19+OQALFy6stR1lVlFd1b1Wxa55TTqfwu/65JNPAkX2jnfeeQeA2bNnt7yOra9c7GeddVbbz9N5FYrVYY51EYusp76rcqprPI/GrEh4v9htt9063NPBVXbujKp1MXHiRGBkdsErrrgCKOYrUV3ofqFr1ZIlS0rtf45iZatrgt7X8RuOcxCVaVk77bQTkM5c1ylH7M3MzMzMMjAu6QD0tBRmQwifojSDWo6RkrFSNhKofK2xuQXUn+/9998v9XlNrLNYpO/ZZ58FYKuttmr7/1dffRWA66+/Hij6Bov6Av/P//wPUETsU58rTaiLqsfdGmusAcDnPvc5IJ49J/Y5+qusHxtttFHpfW2aWJ1oLoEwx7mWV8vWn/70J6DI/X/XXXe1vC/LLbdcy+tYdqiyc340oaU41fc9pDk3NP4htlzqftGEa1JK1XEIsuaaawKw3377ASPPA0XqRXVRdq6OHFWd2VWv1coeo7kGyjrggAMqLR/uT9nzxhF7MzMzM7MM9DRin5r9K4wOh8tr5H0st6if+qsLy2zllVcGYO7cuaOupyweqosmRbVS6paB+tQrUh8+/Zd9Sm/yedBp33RFtxQ11lwAL774Ytvt6LWWP/XUU4HiWlY2CqfPiWlCnWpsiDKvqE98jM6P1LVKNt9887bva+bnGF/T4pQDvez9Ypdddml5vwnHdV1qoVLLk65BulaoT30sUh/S+n/4wx9KfX7Ore1lz+mqkf2q21Oro1or624nxRF7MzMzM7MMjEkf+1h0V1lxdt11VyA9A2G4PUuLldUHPvC3Zzo9Qaa8++67La/rzthphR133BFIzxQoH/zgB9u+7z72hfC7aoZXjdcJafbTsI/9tGnTWl4rc1d4jdp2220r7Y9yU59xxhnAyChZk6PFda8pZctM9xvNAJ3ShPOm7HG3wgorAMVxO3/+/LbLaTtqfUlluGtCGcfO8fC7b7rppkBxjVE2p6985SsAHHrooQC8/vrro37esmXLADj66KNH3Z8mqHoN6XbZVM1OWHXMS/T3XdkdNDMzMzOz/jUuWXE6lXNfsH5zww03APCjH/1o1OVcJ/WjrqnllVtX+e1tpNRxp2htLOtTSBF6/Y1FKKtSxFOqzjMxiOq2SsSuKXWjaqr7cFZHK8TqSK3q++yzT9v1tHzqftHklqlY60Wqp8LLL78MwPbbbw+krxHKvLL33nsD8N5771Xe16YY69buq666CoCnnnqq7f91n9LfuueJI/ZmZmZmZhnoacS+7tOGnpKUFSf2f4tLRUZ++tOfjrq+MlDERt43MeISUzcKdd999wHFOIdPfvKTQDGbqdx0001A0c9Vn6NI/rHHHltjr/OUajmqm/VAffXLLq/PUX/XuvmLB1nZSLtmllWO9AMPPBAoxj9UzWjha1N9sdaVI488Eijy14fLi65l4azAseVt5FhDUQa6D3/4wy3vh2UYzmKqVpW333670n408XfVeH3nqhnvqp43jtibmZmZmWVgXPrYKxfuEUccAVSfvcv9uevbc889gaLPcKwMUzlyxVGyNEVUlPN8u+22A+Dggw9u+RujCIzKWrOdhtEzi1OWm0svvRQorkFlryV1x00o0q/+rpoFsonKXq/VyqEsIC+99FLb5VIZV6ry/aQQnhfhrL6pli2XZaHuGJHwGhW66KKLgPgMy1X3y7onVmezZ88GqreyO4+9mZmZmVkDjUvE/v/+7/+AIiLzyiuvAEVUSzQ7l58sq4s9MW644YZt31d/vO985zuVtuvWk0IqN+3vfve7tstXHZmv/MSO2I+UOg41Y6xmcVywYAEAkydPLrW+qM7ef/99oBgvIZpVWJocqZey2XGef/55AGbNmtWyfKhq1Ev9vhcvXlxpPStmkq2a0Uh8f0ibMWMGACuuuGLL+5odXnR+lJ291GXfPzTr9aJFi4CR10Rl7tLMzuF8EKm5EMQRezMzMzOzDAwNDw8zNDTUflrGLklljojN6qiIjJ5Qe2V4eLh9yotx0K26SGWgmDJlSsv7ypG+7rrrAuM3oj6nugjrQC1Um2++eSebZd68eQCceOKJHW0nZZDqomr/ah2vyy//t0bLHXbYodR6ykMskyZNqvS5MakIzCDVRUzV2RP7VQ51EUodf+pjr2tPSJldbrzxxlG3L028X3QrZ3rV6O1YGaS6GG/KfKfxcjHnnXceUPRqCTlib2ZmZmaWsTGJ2KcMD7d+vJ5CjjnmGABeeOGFXn9+Y540N9hgAwAuueQSoBhRH+bClbGOBgxyXdSdYTPVN7+qJkfDxjtqFepWlG6Q6qKsfok8Vm1JyLEuyopl8JozZ86o6zliP/aznI61QaqLsdLtbIFljxFH7M3MzMzMMtDTiH3qaWXVVVcFihHAcsYZZwBF/6KlS5e23W4Tn/rLikVIJkyYAMBJJ50EwPHHHw/AvvvuW2n7vYou5FgX3RKrU0fD4no9v0Kn8zg4Yt+/mhSxrzu773hHmQexLjptye1Xg1gX/aLb93BH7M3MzMzMMjCmfew7fSpxZLJz/R4taFJd1DVW0TLXRf9wXfSPHOtiUPt/51gXg8p10blu/cZ1xN7MzMzMLANDYUYaMzMzMzMbPI7Ym5mZmZllwD/szczMzMwy4B/2ZmZmZmYZ8A97MzMzM7MM+Ie9mZmZmVkG/MPezMzMzCwD/mFvZmZmZpYB/7A3MzMzM8uAf9ibmZmZmWXAP+zNzMzMzDLgH/ZmZmZmZhnwD3szMzMzswwsDzA0NDQ83jsynoaHh4fGex/EdeG66Beui7gvfelLAPz4xz8e9f+h2PIprov+4broH66L/uG66B+O2JuZmZmZZWBoeHi48U83ftLsH66L/uG6GBl5T0XcY5H6smLbd10U6pZx3daSUA51UbVFqWyZd6uMy8qhLnLhuiikzpdenyeO2JuZmZmZZcARe/yk2U9cF/2jyXURi7i89dZbAKy33npt/3/VVVcB8OKLLwJw3HHHtfzffezjuj0uoVdyqIuyEcXUWJJOPyf8vKpyqItcuC5Gqtq62K1rnSP2ZmZmZmYZWH4sPmSs+kSOd7+mXuhW2XX7ybFq3+MmGe8+wNY7U6ZMAWCrrbZq+//lllsOgEmTJrX9fxgB9XlU6NZ3H5TI/1iqGznvtM992c+xtLH6fTNeUeZB0K06cB97MzMzMzNLGpc+9p0+7Ydi0emyT0U59A3rVpRqvFs9cqiLmG5Fubp9/sQ+J+e6iEmdRzNnzgTiEftwO7fffjsAs2fPBuCdd95p2V5ZTaiLQYm051AXdSP4G264YcvrK6+8ctT1Fy5cCMCCBQsqfU5ZOdRFXePdahL+zmpCXZT9fbTOOusAsOKKKwLFeaLzQOdFVWXrzBF7MzMzM7MMdDViXzXa263IY6dPnv34pNmtyHnVyMx49w/vx7pI6XUEvdecO72Q6vO+0047AbD//vsDsOaaa466PWXRGR4ebtneddddV2m/mlgXKRrPIH/5y1/G5HNzrgsd/1tssQUA5557LgCf+tSnADj11FNHXS/0/PPPA3DsscdW2o8mtbaHyvY46NX4u6q/LZoUsS9r3333BYoyOuiggwBYsmQJABdffDEAjz/++Kjbqfu7g2iZyQAAFTFJREFUyhF7MzMzM7MMjEnEvuzI+o022giACy64AIDTTjtt1M9ThObJJ58E4NZbby2/s/+PfnzSjGXPCMsyVUZyxhlnVNoPbVf5uN98882W/3crz3GoH+uiU+FxvvbaawMwceLEjrYb1mnqWIjVZajJEZjU8az/X3TRRUCR/eaKK64AiroNl5fTTz8dqH4+5lgXqQxBstJKKwFw0003ATB37lwAVl111bbLKRrWq775OdVFWObrr78+UByfEyZMANLnQ+z/1157LQA33njjqJ+bOgaa1Ko41rNdh5o83qHbPRbUYrvyyiu3vL906VIAzjvvPADefvttAJ555pmufK4j9mZmZmZmGegoYt/tWeXOOussoOjfV/bzpk2bBsAbb7xR6fNkEJ80P/CBvz2TqdWi035zsbK94YYbgKJPca8NYl2kyjqMau2+++4AHHnkkZW2G9t+7HNCX/7ylwH4whe+MOrnyvz58weuLjpVNmIfUh9iRV5i9thjj1G306TIZExYNspApIxEsTI65phjAHjhhRd6t3MMRl1UnVlWrR9qDYktX5fWV6Ry2bJlpZZPGYS6SOnWXBbqx90ptbKUpf3P6X5RNXKv8T5hrxHVyTXXXNPyfljHjz32GFC06HaaIcwRezMzMzOzDHQ082wqMpl66gmfYsI+k7HPS213UPIh91K3vut+++0HwGqrrQaUj/bmLBbVjZW53tdI+X322afU51Stw7qz3g16Vp9eqHotK0vjIBShse6VrXJFq499TBPuA1W/YyxS3y277rorAA899BBQ9C2W8Bho0j08dY2JRfRnzJgBFD0dwkh7KoIfi8yHv8vk05/+NAAf+9jHWvYjR3V7QMTKLrVebLm610ZH7M3MzMzMMjAufezPP/98oMiCU5f61N95550tr8vuhwxiPz31sa+bCUjK1uHdd98NwC677NKyXref2gexLlJlceCBBwKw2267AbD66qt3Ye+qR7WUFUdRY/V3jW1vEPpMVu1LHL7fbZqZMxY1dh/7NJXNlClTADjuuONa/h8ro1deeQWATTfdFChysMc0IftH2ev7/Pnz275fdaxJjLYTy/6he3f4fqqOBqkuYspmCFIE/tlnnwWKLFCd/o5K7Y+k5iQYhPtFqGp2pm984xsA7LDDDkCRPUr31LrXFJ0Xjz76KACLFy8G6o9FccTezMzMzCwDHfWxD4VPD2ussQYABx98MABrrbUWkH7CPPPMM4GiP17sc1ZZZRWg6Nd09NFHA0VkcpClosB//etfgSI/6p/+9KeW/+up+qWXXmp5X/1Q5Z577gGK6FjZ/Sq7nzlLRa3Up159GXuV+78s5ahWH8kwYj+IdditiGLZ9VIRnb333nvU9d3HfqRYWWpcj6SOT80hoPuMxfsK694Z3g96fQ1QlFmZjsL9Upa3q6++etTtDOK1KiZ1TTn00EOBIqtZuF6o05lkqxrkukiNi5MHH3wQKMYZ6P/h3DF17+06L7bccktg5BiUqhyxNzMzMzPLQFcj9iE95ajfqYRPqFruz3/+MwDbbrstUPTpCteXFVZYoWV7l112Wdf2vd+p7BQNDkfAq+xCt9xyC1CUmWYhTT1hhjOnWTwapj70qRHy4XpjZWiob7pCVla1T2SnZVs189fhhx8OwLx589puL5xx1hmJRpo8eTIAhxxySFe3O8iRxarKZu7SPCXdiuouWbIEKFrrU/sTbk/5wGP3/JzrMPxusUi9qOw0I/N3vvMdYOS9Wq33O+64IwBTp05tWT/l/fffB0Ze03Kui9A222wDlM9W85GPfAQoftOGVBd33HFHy/uK3L/88ssArLvuurX21xF7MzMzM7MM9DRi/4Mf/AAosuCInnYWLVoEwGc+8xkAJkyYAMDEiRP/tnPLL992fXnnnXcAmD17djd3e6C89tprAFx00UUt78eeKLfeemugiIyozGNuu+02AK666qq2223SU3uobn+6VBStV1Fc1eW//Mu/AHDiiSf25HN6qWyfyFgZqmUrzLSiSHv4fvg69v7ChQuBdB/7cP+6nSd/kKSuUXXHT2i80L333ltq/RyvYanvtNlmm3W0XZWhsneE9+K///u/B4rIpOi17jux41/L6bxasGBBy3I51lmo7DXhwgsvBOCJJ54AYOedd267nMo0tV2Nvdprr72AkedRjteq1P1Cv5dS312/aZUVLTUTeXg8f+UrX2n5fzgmyzPPmpmZmZk1SE8i9uqTpaecmG9961str9X/VNk7QnpaUc5bvb799ttr72u/imVQqfu0vMUWWwBw1FFHAUUfyNgToLLlKFIf7leoCVGwmLKZVKquV1Xqc/TUH4t+DUI0LNXHXq+VUSXM9qRMReF3VIQlzC5Vto70OYrYhNmoQmVnLc5RrEx1jbr00kuBoo9xKHUt1LVL95NHHnmk/s4OqNS5fM4554y6fOy1xj1obNeNN95Yab++/vWvA/Duu+8CxYy0sf1UXnuNuzv99NMrfd4giNXVYYcdBozMEKR78vXXXw/Aww8/POr2h4fLpdfX52uuDSvuFxLW0auvvgrATjvtBBTXsNR9qmyd1OWIvZmZmZlZBroSsa8bTVZO0H322QeAVVddtdR6iobFogWDGIkMxfLF1zV9+nQgnq0gFM6QmSrLJvcVjlFUK5xpdqwi9aIoVyx3+iDVWdk+9tdddx1Q1EGnn1e2jDbffPOWv1b++qvI4/HHH9/2/8px/uSTT7ZsN2bGjBlAMVYr3J8mCL+r8sdXzSKlSP2cOXM62h/NMaNjQX3tP/WpTwFFVpBwP3bffXegma0vIUXyf/vb3wLxsSSx8UGhMFNXk1S9FoTXMs21pDFakrpPKVNRquW2bt04Ym9mZmZmloGuROzDp5633noLiI981+uTTjqp5XVse+H7ijzGlhukyHxM1e+QevLU6G5FwxQZSVG0Wesr+0FMDmWfkhr3oCix+o9WjQooz3zYD69s2Wo9zVEQe+ofxMjleJ3zqfELZSP7qZlnczp/yrY6ahZUReRjlM9b2ToUkY9RZDNVJ4PYotupsuN+9FoZVZSFLbadVL768H3NsKnfAuHMnhYvC407mD9/fq3tfuMb3wDKz3KaQ0+IqnTvDGdKFo13KHte6HfXMcccA4wc65JStswdsTczMzMzy0BHEfvU7IvKh3rkkUe2LB+qGjlU1Cs2ensQI5GdSpWt+q9qdrrUk+LcuXMB2H///VveV9YP5WctG8m0tE4j9aJIfSpbwiCPi0jtu47f2HLdOj67XYY5nTdlo7bKPKFxQKFY2SqapvtB2f1IbTcnZY//WFkoT31szouyrSGp5RU1PuKII0ptNydjfa5/9atfBeCKK64Ydbkm1UFVKptYK2Os7JQJUmOwUnWva1vVvvaO2JuZmZmZZaAnWXHCbDXLli0D4Jvf/GY3Pq70jJk5RL2qCiMxYRlodjqNylb+7pAyFIXrb7LJJm0/L5Rz2ZfNzDJt2jSgyMUc88lPfhKon9s2rINTTjkFKKJtKYNYV7FI4E9/+lNgZIat8BqlXOepLBupSImizam+xTJz5kygmCla+xHKIXJfNuK38sorAyNnXQyplfHkk08GYKONNmq7nMpMcwsoZ7oyJYXL5azs9Tl23GpMlcbNpXQ6C7fqKqTxEk2oM1EGIs2toXFuZYV1rx4UH/zgByutV/Z+N8iqtrxqeWVrKis1trHsfSR1f3DE3szMzMwsAz3JihM+TTzwwANAkfPzrrvuarud1FOTomepCGgTVc17v+aaawJFtCyccTNGdaS5BNZbb71S+5PT072UfYpORX1PPfVUAFZYYYVan6/PU6aVs88+u+X/dWfGHUQapxAzadIkAJ5//nmgfIRGyynSrtmxL7/8cqB6Gf7xj38EYM899wTgtttuq7T+IEqVUer6r0h92e0po9fGG29ccg/tzDPPBGDJkiUAPPfcc5XWj7UYpzLd6byK5WRvglgZbbDBBkCRDUrX+dhcGWHZz5o1C4Bbb7215XPch75QtSx0vY5lN+vU//3f/wHFvA9VOWJvZmZmZpaBrkTsQ7FIivoj6alcfSBj/f1uvvlmoOhr1sT+3GWVzS2r/7/66qtAkXNd/feU0Uh5hddee+2W9TfccEMADj30UKCIAihLzrrrrtvyOvzccH8HWd2MKJoJU7lst9tuu1LrxT7n8ccfB0bmI86hjGPqlr0i9eF2UtQKooi/qA5j+xd7rTpfsGABAHfccQcA77//fqn9GSSxMlYk/eqrry61fGr74bEQOy+aTK2Hsdznjz76KFBk1Kob1U3dh9TXWFlC7r//fqBoSU5tN2fh8azjWBlYLr30UgCuvPLKtsvrr1pdfvGLX7SsH/uc8P0mKDuHUkgtWcpjX3X9ULjeT37yE6DIcBdyHnszMzMzswboalYciT21KNp74IEHtl1P1H9JkfrY59hIVZ9ANWOa+jgqoqlsOYqg7LTTTgBsvfXWQFGXmsn2jTfeAODOO+8ERkbsc1I1d7OobnbYYQcAJk+eXOvztJ2FCxcCRT9vZT3IWeoaoChWzGc/+1mgGPcTo+P7P//zP4F4f9YYnVdqlRRlSpo4cSJQHAO/+tWvgKI1M4esOCm6D0i3vqvKTi3EDz74YNvlmjSDZlkqC7XEli2TVB/60IorrggU55XGbIV0jdOsw+ov3kR33303MPKaEivjtdZaC6iea90K4RwZOh+UyS4cE5JqBVEmr/Den2qBrnptcsTezMzMzCwDHUXsY08l4dNH2C87NtJeOZ3Dfks2dmJPjAcccABQ1GWTo1udzjYa9tMuOwtkuLwiMepLGVsu1IS6S0UQNYOy+p/KNttsA1SP0IeuueYaIJ7lI8wmpfklJIc6SkXEUzPGht577z2gaBVU64uy39SVQ1lXpe+86667AkVGrnnz5gHFNeXv/u7vam1XVPdq+dXYEmVji1G/8i233BIYGanPoc5Sc87IFltsAVS/Jn37298GRs7Vkfq8JqqaHU1/X3/9dSDdu0SZ72Lj6dTjQa3uutbV5Yi9mZmZmVkGetrHXhlVvvrVrwJFFoRYfzr1827yKO1uSUWBY9G0bvW7a0L/1apZBY477jhgZB/JsrNAivpaKorlvpKFWBYBCeusbBQsdfyqT73GT4R95UNhhCeU83kjysSlzFypnNCKZl1xxRUAXHjhhUA6+hvT5PuMylCtHuGspmussQZQnE+rrbbaqNtLzU6qll7N25BaTy1n4czoOdVR6rtorgzN3zBhwoRS2z399NOBkedT7L6SU5l2qurvIGX00vVcY0E23XRTAD760Y8C8VmyRb+JU+dZWY7Ym5mZmZlloFLEvmpk8De/+Q1QROrDJ8Ph4WGgiArMmDEDKPrXhZ/rJ8vyUlHg1PuyyiqrAEUWAwmPBWVmUfSt7PZzkPqOOs7Lbid1nr377ruV1suxDmLfVeN01Pdx5syZQOdloM+7/vrrgZGZJpSN55BDDml5P1Y3qWhxDnWWGueg6/4ee+xRa7v77rsvUPQLX2655VqW+9rXvgbkUZZVdes7K0qsa5hmdg5bW/Ra94uqvxWWLVsGFOdVWJcyiL8FyrYMhd9Ns4/ed999bZdXWanswvdTBqkMe6Vbrd06P4466igAzjrrLCBdxupLH2a067Q10RF7MzMzM7MM1OpjX/bpIZXdRpEaPeUsXry4zu7YKOr2odfyioppZH3Yb0+tK6ecckrH+5qbyy67rNZ6sfNLmYnCCE24nvvcF7odlVIdlFU120Ioh6jaeH0HZXz54Q9/CKRbBnIo65huRbrD1keVsSL2t9xyS63t6t7/61//GojPgzKI17hUmada82KefPJJYOS9V/28rT7VmVqoyra6q+eCZnbW+jF77703AFOnTgWq3y9ix5Yj9mZmZmZmGagVse80uhSur76WZfuGWe+oDjUr5G677QaM7GOvvszKZJQyiH0j61L0Si1RKakyUY7bWKS+6vZyVLePetVc6lU1sS56peqs2oqGnXHGGT3bp36XKiPNhPz73/8egBNOOKHS9sM5BFTmZZ133nkAPProo0B6xvJBitRL1X1WZpQwe1pIYxLDmZX9O6q6VJZAlentt9/e8n5s/TBSHy6/3377AXDXXXe1/X+o7JwH4oi9mZmZmVkGepLHXn2+lLtWwqcS5dJ96623Rt2e1Rcry2233RYonvrlsMMOA4poQRiRUR3uvvvuADz77LOV9qcJkXvNOpoSKwOdD6qLspH6JpStVJ2lN1YmVaO5se01qeyranK++PEWniex4/QDH/hbjE95ucPsTqntlaXtKxvIQw891PK6bMaYQYzcx4Tj2dRKHrv3prZj1aXGOZx99tlAMZuv/n/ooYeW2r7qRvMzbLDBBkAxRjHVYuCsOGZmZmZmDTQ0PDzM0NBQqSG/qacH5at/6qmn2i4XPgXFshSM9ZPn8PDw6EOXx1DZukhJ1dXs2bOBeGQmRRmPbrvttrb/r1uHg1QXYRmrv7Zyp0vZp+9XXnkFKKLIqSxRvc6kMgh10WmkvNeRvybVRa/F6iqWx15RtnPOOQcYmSu6rkGqi05bmMJW97I0r4M8//zzQPx+IWVnRNf7g1AXse+y9tprA/Db3/4WgOnTpwPpOWPkxhtvBEbOHSP+HZXWaYQ8dn6Ex78sWLAAgIULF4663U7rzhF7MzMzM7MMdKWPvSiTSuxp45JLLgGKvMJPP/10Nz/exsCcOXOA7kfqc/Daa68BRaR9nXXWAdJRAEWzdF5o/Zz7lXZL1eOtbj9Vl/34i0XV1lprLQA222yzluWfeOIJoHuR+kFUNYd6uLyuTVXFsqWVjYzWnSl9kEycOBFIz9AcuvXWW4Eis4rH93Su7FwCqfND66WyBfa6rhyxNzMzMzPLQFcj9spt/sILL7T9v3LVOlI//tSf74EHHgDSswQrm0Gqj6Q0MYqgLE/6qywHK6+88qjr6byYNGkSUJwfqf6lVl3dsnOZ94+wLpYsWdLyN8ZZRdL5t7u9/ar/j8m5bsq2zCpSP2vWrJb3cy6bXut2drPUuNKxqitH7M3MzMzMMlArK06vdJq7s65BHM2dUjVjSr9EhQehLsa7v7XPi+ZxXZTX66hwDnXRq2uYM7GMlMq8EmZRC2l23n4fKzIIdZHS6XnRL60njtibmZmZmWWgUsQ+lMvTTQ5PmlWVrTtHYNJyOQ9Cg1gXuXJd9A/XRf9wXfQP10X/cMTezMzMzCwDQ8PDjX6wMTMzMzPLwv8H90hIa4bA2lsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x1200 with 96 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(100.0, 120.0), dpi=10)\n",
    "plt.set_cmap('binary')\n",
    "for i,image in enumerate(adv_data):\n",
    "    plt.subplot(12, 8, i+1)\n",
    "    # plt.subplots_adjust(wspace=0.2, hspace=0.2)\n",
    "    image = np.squeeze(image, 0)\n",
    "    image = image/np.amax(image)\n",
    "    image = np.clip(image, 0, 1)\n",
    "    plt.imshow(image, cmap='gray', interpolation='nearest')\n",
    "    plt.axis('off')   \n",
    "print(\"Test images after attacking showed below:\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对抗性防御\n",
    "\n",
    "`NaturalAdversarialDefense`（NAD）是一种简单有效的对抗样本防御方法，使用对抗训练的方式，在模型训练的过程中构建对抗样本，并将对抗样本与原始样本混合，一起训练模型。随着训练次数的增加，模型在训练的过程中提升对于对抗样本的鲁棒性。NAD算法使用FGSM作为攻击算法，构建对抗样本。\n",
    "\n",
    "### 防御实现\n",
    "\n",
    "调用MindArmour提供的NAD防御接口（`NaturalAdversarialDefense`）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:20,485 [<ipython-input-13-065f75174bd9>:29] [demo] accuracy of TEST data on defensed model is : 1.0\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:20,498 [<ipython-input-13-065f75174bd9>:44] [demo] accuracy of adv data on defensed model is : 0.3645833333333333\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:20,498 [<ipython-input-13-065f75174bd9>:46] [demo] defense mis-classification rate of adversaries is : 0.0\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:20,499 [<ipython-input-13-065f75174bd9>:48] [demo] The average confidence of adversarial class is : 0\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:20,499 [<ipython-input-13-065f75174bd9>:50] [demo] The average confidence of true class is : 0\n",
      "[INFO] MA(2622:139980130522944,MainProcess):2020-12-01 16:56:20,500 [<ipython-input-13-065f75174bd9>:53] [demo] The average distance (l0, l2, linf) between original samples and adversarial samples are: (-1, -1, -1)\n"
     ]
    }
   ],
   "source": [
    "from mindspore.nn import SoftmaxCrossEntropyWithLogits\n",
    "from mindarmour.adv_robustness.defenses import NaturalAdversarialDefense\n",
    "\n",
    "\n",
    "loss = SoftmaxCrossEntropyWithLogits(sparse=False, reduction='mean')\n",
    "opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)\n",
    "\n",
    "nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,\n",
    "                                bounds=(0.0, 1.0), eps=0.3)\n",
    "net.set_train()\n",
    "nad.batch_defense(np.concatenate(test_images), np.concatenate(test_labels),\n",
    "                  batch_size=32, epochs=20)\n",
    "\n",
    "# get accuracy of test data on defensed model\n",
    "net.set_train(False)\n",
    "acc_list = []\n",
    "pred_logits_adv = []\n",
    "for i in range(batch_num):\n",
    "    batch_inputs = test_images[i]\n",
    "    batch_labels = test_labels[i]\n",
    "    logits = net(Tensor(batch_inputs)).asnumpy()\n",
    "    pred_logits_adv.append(logits)\n",
    "    label_pred = np.argmax(logits, axis=1)\n",
    "    acc_list.append(np.mean(np.argmax(batch_labels, axis=1) == label_pred))\n",
    "pred_logits_adv = np.concatenate(pred_logits_adv)\n",
    "pred_logits_adv = softmax(pred_logits_adv, axis=1)\n",
    "\n",
    "LOGGER.info(TAG, 'accuracy of TEST data on defensed model is : %s',\n",
    "             np.mean(acc_list))\n",
    "acc_list = []\n",
    "for i in range(batch_num):\n",
    "    batch_inputs = adv_data[i * batch_size: (i + 1) * batch_size]\n",
    "    batch_labels = test_labels[i]\n",
    "    logits = net(Tensor(batch_inputs)).asnumpy()\n",
    "    label_pred = np.argmax(logits, axis=1)\n",
    "    acc_list.append(np.mean(np.argmax(batch_labels, axis=1) == label_pred))\n",
    "\n",
    "attack_evaluate = AttackEvaluate(np.concatenate(test_images),\n",
    "                                 np.concatenate(test_labels),\n",
    "                                 adv_data,\n",
    "                                 pred_logits_adv)\n",
    "\n",
    "LOGGER.info(TAG, 'accuracy of adv data on defensed model is : %s',\n",
    "            np.mean(acc_list))\n",
    "LOGGER.info(TAG, 'defense mis-classification rate of adversaries is : %s',\n",
    "            attack_evaluate.mis_classification_rate())\n",
    "LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',\n",
    "            attack_evaluate.avg_conf_adv_class())\n",
    "LOGGER.info(TAG, 'The average confidence of true class is : %s',\n",
    "            attack_evaluate.avg_conf_true_class())\n",
    "LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '\n",
    "            'samples and adversarial samples are: %s',\n",
    "            attack_evaluate.avg_lp_distance())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 防御效果\n",
    "\n",
    "通过输出信息可以看出，使用NAD进行对抗样本防御后，模型对于对抗样本的误分类率从94.79%降至0，模型有效地防御了对抗样本。同时，模型对于原来测试数据集的分类精度达100%，使用NAD防御功能，并未降低模型的分类精度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "以上便完成了MindArmour在模型安全上的应用体验，我们通过本次体验理解了对模型攻击的概念和原理，了解了如何使用`FastGradientSignMethod`接口对模型攻击和`NaturalAdversarialDefense`接口实现对抗性防御。"
   ]
  }
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